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Computed theoretical power for N=100 and N=200 scenarios

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This commit is contained in:
Corentin Choisy
2024-02-19 18:35:26 +01:00
parent ac9189d26a
commit 238852b08b
704 changed files with 261610 additions and 187 deletions
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39
Modules/ado/plus/m/maplist.ado Normal file
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program def maplist, rclass
*! NJC 1.0.0 22 August 2000
version 6.0
gettoken list 0 : 0, parse(",")
if "`list'" == "" | "`list'" == "," {
di in r "nothing in list"
exit 198
}
syntax , Map(str asis) [ Global(str) Noisily Symbol(str) ]
if "`symbol'" == "" { local symbol "@" }
if !index(`"`map'"',"`symbol'") {
di in r "map( ) does not contain `symbol'"
exit 198
}
if length("`global'") > 8 {
di in r "global name must be <=8 characters"
exit 198
}
tokenize `list'
while "`1'" != "" {
local result : subinstr local map "`symbol'" "`1'", all
capture local 1 = `result'
if _rc {
di in r "inappropriate map?"
exit _rc
}
local newlist "`newlist'`1' "
mac shift
}
if "`noisily'" != "" { di "`newlist'" }
if "`global'" != "" { global `global' "`newlist'" }
return local list `newlist'
end

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Modules/ado/plus/m/maplist.hlp Normal file
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.h listutil

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Modules/ado/plus/m/meta.dlg Normal file
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/*
*! meta dialog version 1.0.1, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for meta version 2.06, Mar 1998, Stephen Sharp, stephen.j.sharp@gsk.com
*! Jonathan Sterne, jonathan.sterne@bristol.ac.uk
Fixed and random-effects meta-analysis, with graphics
-----------------------------------------------------
Syntax: meta { theta | exp(theta) } { se_theta | var_theta | ll ul [cl] }
[if exp] [in range] [ , id(strvar) var ci graph(f|r|e) eform print
ebayes level(#) fmult(#) boxysca(#) boxshad(#) cline ltrunc(#) rtrunc(#)]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-analysis of Effects (&meta)" "db meta"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help meta")
RESET res1, label("Reset")
DIALOG main, label("meta 2.06 - Meta-analysis of Effects") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 330 _ht5h, ///
label("Type of Data:")
RADIO r_se 15 25 100 ., ///
label("Theta, SE") first ///
onclickon(script main_se_on) ///
onclickoff(script main_se_off) ///
option(NONE)
RADIO r_var 115 25 100 ., ///
label("Theta, Var") middle ///
onclickon(script main_var_on) ///
onclickoff(script main_var_off) ///
option("var")
RADIO r_ci 215 25 110 ., ///
label("exp(Theta), CI") last ///
onclickon(script main_ci_on) ///
onclickoff(script main_ci_off) ///
option("ci")
TEXT tx_se 15 45 320 ., ///
label("Vars for theta, se(theta), in that order")
VARLIST vl_se @ _ss @ ., ///
label("Vars for theta, se(theta)")
TEXT tx_var @ 45 320 ., ///
label("Vars for theta, var(theta), in that order")
VARLIST vl_var @ _ss @ ., ///
label("Vars for theta, var(theta)")
TEXT tx_ci @ 45 320 ., ///
label("Vars for exp(theta), CI_LL, CI_UL, in that order")
VARLIST vl_ci @ _ss @ ., ///
label("Vars for exp(theta), CI_LL, CI_UL")
CHECKBOX cb_id 10 100 100 ., ///
label("ID Variable:") ///
onclickon(main.vn_id.enable) ///
onclickoff(main.vn_id.disable)
VARNAME vn_id 110 100 230 ., ///
label("ID Variable") ///
option("id")
CHECKBOX cb_eform 10 130 150 ., ///
label("Use exp(theta)") ///
option("eform")
CHECKBOX cb_print 10 150 150 ., ///
label("Print details") ///
option("print")
CHECKBOX cb_ebayes 10 170 300 ., ///
label("Generate Empirical Bayes variables") ///
option("ebayes")
CHECKBOX cb_level 220 130 75 ., ///
label("CI Level:") ///
onclickon(main.ed_level.enable) ///
onclickoff(main.ed_level.disable)
EDIT ed_level 300 @ 40 ., ///
label("Level") numonly default(95) ///
option("level")
END
DIALOG graph, tabtitle("Graph Options")
BEGIN
GROUPBOX gb_graph 10 10 330 _ht2h, ///
label("Graph Estimates:")
RADIO r_none 15 30 65 ., ///
label("None") first ///
onclickon(program graph_off) ///
onclickoff(program graph_on) ///
option(NONE)
RADIO r_fixed 80 30 65 ., ///
label("Fixed") middle ///
option("graph(f)")
RADIO r_random 145 30 75 ., ///
label("Random") middle ///
option("graph(r)")
RADIO r_ebayes 230 30 95 ., ///
label("Emp. Bayes") last ///
option("graph(e)")
CHECKBOX cb_cline 15 80 150 ., ///
label("Draw Estimate line") ///
option("cline")
CHECKBOX cb_fmult 200 80 95 ., ///
label("Font scale:") ///
onclickon(graph.ed_fmult.enable) ///
onclickoff(graph.ed_fmult.disable)
EDIT ed_fmult 300 @ 40 ., ///
label("Font scale") numonly default(1) ///
option("fmult")
CHECKBOX cb_boxy 200 110 95 ., ///
label("Box yscale:") ///
onclickon(graph.ed_boxy.enable) ///
onclickoff(graph.ed_boxy.disable)
EDIT ed_boxy 300 @ 40 ., ///
label("Box yscale") numonly default(1) ///
option("boxysca")
CHECKBOX cb_boxs 200 130 95 ., ///
label("Box shade:") ///
onclickon(graph.ed_boxs.enable) ///
onclickoff(graph.ed_boxs.disable)
EDIT ed_boxs 300 @ 40 ., ///
label("Box shading") numonly default(0) ///
option("boxshad")
CHECKBOX cb_ltrunc 15 110 95 ., ///
label("Left truncate:") ///
onclickon(graph.ed_ltrunc.enable) ///
onclickoff(graph.ed_ltrunc.disable)
EDIT ed_ltrunc 125 @ 40 ., ///
label("Left truncate") numonly ///
option("ltrunc")
CHECKBOX cb_rtrunc 15 130 105 ., ///
label("Right truncate:") ///
onclickon(graph.ed_rtrunc.enable) ///
onclickoff(graph.ed_rtrunc.disable)
EDIT ed_rtrunc 125 @ 40 ., ///
label("Right truncate") numonly ///
option("rtrunc")
GROUPBOX gb_gopts7 10 155 330 _ht1h, ///
label("Allowed Graph7 Options:")
EDIT ed_gopts7 15 175 320 ., ///
label("Graph7 Options")
END
INCLUDE ifin
SCRIPT main_se_on
BEGIN
main.tx_se.show
main.vl_se.show
main.tx_se.enable
main.vl_se.enable
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_se_off
BEGIN
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
END
SCRIPT main_var_on
BEGIN
main.tx_var.show
main.vl_var.show
main.tx_var.enable
main.vl_var.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_var_off
BEGIN
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_on
BEGIN
main.tx_ci.show
main.vl_ci.show
main.tx_ci.enable
main.vl_ci.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_off
BEGIN
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
PROGRAM graph_on
BEGIN
call graph.cb_cline.enable
call graph.cb_fmult.enable
call graph.cb_boxy.enable
call graph.cb_boxs.enable
call graph.cb_ltrunc.enable
call graph.cb_rtrunc.enable
call graph.gb_gopts7.enable
call graph.ed_gopts7.enable
if graph.cb_fmult {
call graph.ed_fmult.enable
}
if graph.cb_boxy {
call graph.ed_boxy.enable
}
if graph.cb_boxs {
call graph.ed_boxs.enable
}
if graph.cb_ltrunc {
call graph.ed_ltrunc.enable
}
if graph.cb_rtrunc {
call graph.ed_rtrunc.enable
}
END
PROGRAM graph_off
BEGIN
call graph.cb_cline.disable
call graph.cb_fmult.disable
call graph.cb_boxy.disable
call graph.cb_boxs.disable
call graph.cb_ltrunc.disable
call graph.cb_rtrunc.disable
call graph.gb_gopts7.disable
call graph.ed_gopts7.disable
if graph.cb_fmult {
call graph.ed_fmult.disable
}
if graph.cb_boxy {
call graph.ed_boxy.disable
}
if graph.cb_boxs {
call graph.ed_boxs.disable
}
if graph.cb_ltrunc {
call graph.ed_ltrunc.disable
}
if graph.cb_rtrunc {
call graph.ed_rtrunc.disable
}
END
PROGRAM command
BEGIN
put "meta "
if main.r_se {
varlist main.vl_se
}
if main.r_var {
varlist main.vl_var
}
if main.r_ci {
varlist main.vl_ci
}
INCLUDE _ifin_pr
beginoptions
option radio(main r_se r_var r_ci)
optionarg main.vn_id
option radio(graph r_none r_fixed r_random r_ebayes)
option main.cb_eform
option main.cb_print
option main.cb_ebayes
option graph.cb_cline
optionarg graph.ed_fmult
optionarg graph.ed_boxy
optionarg graph.ed_boxs
optionarg graph.ed_ltrunc
optionarg graph.ed_rtrunc
put graph.ed_gopts7
endoptions
END

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{smcl}
{* 12apr2004}{...}
{hline}
help for {cmd:meta_dialog}{right:(SJ4-2: pr0012)}
{hline}
{title:Submenu and dialogs for meta-analysis commands} {result:(Stata 8 only)}
{title:Description}
{p 4 4 2}
The {cmd:meta_dialog} package provides Stata 8 dialog boxes ({cmd:.dlg} files)
and the commands needed to create a {hi:Meta-Analysis} submenu that contains
the publicly available meta-analysis commands. The 14 commands included
in this package that may be placed on the menu are {cmd:meta}, {cmd:metan},
{cmd:metap}, {cmd:metareg}, {cmd:metacum}, {cmd:funnel}, {cmd:metafunnel},
{cmd:labbe}, {cmd:metannt}, {cmd:metaninf}, {cmd:metainf}, {cmd:galbr},
{cmd:metabias}, and {cmd:metatrim}.
{p 4 4 2}
The menu commands (shown below) are placed in your personal Stata
{cmd:profile.do} file. They create the submenu on the
StataCorp-defined {hi:User} menu.
{p 4 4 2}
You can determine if you have defined a {cmd:profile.do} file by starting
Stata and observing whether a line of the form
{p 8}{result:running C:\data\stata\profile.do} ...
{p 4 4 2}
appears on the screen as part of the initiation sequence. If it does,
add the commands below to that file and resave the file. If the
line does not appear, you have not defined a profile. Create a
plain text file containing the commands below, name it {cmd:profile.do}, and
save it somewhere in the Stata path.
{p 4 4 2}
You may choose not to create the menu and run the dialogs directly
from the Stata 8 command line via the {cmd:db {it:name}} command. If so,
{col 5}{hline 69}
{col 5}command...{col 19}runs...{col 36}description...
{col 5}{hline 69}
{col 5}{dialog meta:db meta}{col 19}{help meta:meta 2.06}{col 36}Meta-analysis of Effects
{col 5}{dialog metan:db metan}{col 19}{help metan:metan 1.74}{col 36}Meta-analysis of Binary and Continuous
{col 5}{dialog metap:db metap}{col 19}{help metap:metap 2.0.0}{col 36}Meta-analysis of p-values
{col 5}{dialog metareg:db metareg}{col 19}{help metareg:metareg 1.06}{col 36}Meta-analysis Regression
{col 5}{dialog metacum:db metacum}{col 19}{help metacum:metacum 1.02}{col 36}Cumulative Meta-analysis
{col 5}{dialog funnel:db funnel}{col 19}{help funnel:funnel 1.04}{col 36}Metan-based Funnel Graph
{col 5}{dialog metafunnel:db metafunnel}{col 19}{help metafunnel:metafunnel 1.02}{col 36}Funnel Graph (vertical)
{col 5}{dialog labbe:db labbe}{col 19}{help labbe:labbe 1.21}{col 36}Metan-based L'abbe Graph
{col 5}{dialog metannt:db metannt}{col 19}{help metannt:metannt 1.0}{col 36}Metan-based NNT
{col 5}{dialog metaninf:db metaninf}{col 19}{help metaninf:metaninf 1.0.1}{col 36}Metan-based Influence Analysis
{col 5}{dialog metainf:db metainf}{col 19}{help metainf:metainf 3.0.0}{col 36}Meta-based Influence Analysis
{col 5}{dialog galbr:db galbr}{col 19}{help galbr:galbr 2.0}{col 36}Galbraith Plot for Heterogeneity
{col 5}{dialog metabias:db metabias}{col 19}{help metabias:metabias 1.2.2}{col 36}Publication Bias in Meta-analysis
{col 5}{dialog metatrim:db metatrim}{col 19}{help metatrim:metatrim 1.0.5}{col 36}Trim and Fill Analysis
{col 5}{hline 69}
{title:Menu creation commands}
{p 4 4 2}
Put the following commands in your {cmd:profile.do} (the lines are long and may wrap on the screen):{input}
{hline}{asis}
if _caller() >= 8 {
window menu clear
window menu append submenu "stUser" "&Meta-Analysis"
window menu append item "Meta-Analysis" "Of Binary and Continuous (meta&n)" "db metan"
window menu append item "Meta-Analysis" "Of Effects (&meta)" "db meta"
window menu append item "Meta-Analysis" "Of p-values (meta&p)" "db metap"
window menu append item "Meta-Analysis" "Cumulative (meta&cum)" "db metacum"
window menu append item "Meta-Analysis" "Regression (meta&reg)" "db metareg"
window menu append item "Meta-Analysis" "Funnel Graph, metan-based (f&unnel)" "db funnel"
window menu append item "Meta-Analysis" "Funnel Graph, &vertical (metafunnel)" "db metafunnel"
window menu append item "Meta-Analysis" "L'abbe Graph, metan-based (&labbe)" "db labbe"
window menu append item "Meta-Analysis" "NNT, metan-based (metann&t)" "db metannt"
window menu append item "Meta-Analysis" "Influence Analysis, metan-based (metan&inf)" "db metaninf"
window menu append item "Meta-Analysis" "Influence Analysis, meta-based (metain&f)" "db metainf"
window menu append item "Meta-Analysis" "Galbraith Plot for Heterogeneity (&galbr)" "db galbr"
window menu append item "Meta-Analysis" "Publication Bias (meta&bias)" "db metabias"
window menu append item "Meta-Analysis" "Trim and Fill Analysis (metatrim)" "db met&atrim"
window menu refresh
}
{smcl}
{hline}
{sf}{txt}
{title:Notes}
{p 4 4 2}
Dialogs are available only in Stata 8 or later; thus, the leading
{cmd:if _caller() >= 8 {c -(}} and trailing {cmd:{c )-}} lines above are
needed only if you also run Stata 7. Leaving these lines in will not cause
problems.
{p 4 4 2}
The easiest way to capture these commands is to open this help file in your
text editor, copy the lines, and then paste them into {cmd:profile.do}.
{title:Author}
{p 4 8 2}
Thomas J. Steichen, steichen@triad.rr.com
{title:Also see}
Manual: {hi:[P] dialogs; [P] window menu; [R] db}
{p 4 13 2}Online: help for {help dialogs}; {help window}; {help db}

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Modules/ado/plus/m/metabias.ado Normal file
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*! metabias version 1.2.2 07feb01 TJS (STB-61: sbe19.4)
program define metabias, rclass
version 6.0
* version 1.0.0 24oct97 TJS STB-41 sbe19
* version 1.1.2 21apr98 TJS Unweighted Egger analysis only STB-44 sbe19.1
* version 1.1.3 1oct99 TJS fixed stratified bug STB-44 sbe19.1
* version 1.2.0 17feb00 TJS Version 6 and gweight option (STB-57: sbe19.2)
* version 1.2.1 31jan01 TJS touched for version 7.0
*
*! syntax: metabias varlist [if] [in]
*! [ , Graph(str) GWeight Vari CI BY(varname) LEvel(real 95) * ]
if ("`*'" == "") {
di "Syntax is:"
di in wh "metabias " in gr "{ theta { se | var } | " _c
di in gr "exp(theta) | ll ul [cl] } [" _c
di in wh "if " in gr "exp] [" in wh "in " in gr "range]"
di in gr " [ " in wh ", by(" in gr "by_var"in wh ")" _c
di in gr " { " in wh "v" in gr "ar | " in wh "ci" in gr " } " _c
di in wh "g" in gr "raph" in wh "(" in gr "{ " in wh "b" _c
di in gr "egg | " in wh "e" in gr "gger }" in wh ")"
di in wh " gw" in gr "eight" in wh " lev" _c
di in gr "el" in wh "(" in gr "#" in wh ") " in gr "graph_options ]"
di _n in gr " where { a | b |...} means choose" _c
di in gr " one and only one of {a, b, ...}"
exit
}
* Setup
syntax varlist(numeric min=2 max=4) [if] [in] /*
*/ [ , Graph(str) Vari CI BY(varname) LEvel(real 95) * ]
* Note: option GWeight is passed via * directly to the graphics subs
marksample touse
tokenize `varlist'
tempvar byg theta setheta var w sw vt wtheta swtheta
tempvar zz Ts wl swl RRm bylabel
tempname k ks sdks p zu pcc zcc c sks svks sk oe sbv sv
* v7 fix
tempvar bylabl
local theta `1'
if "`3'" == "" { local setheta `2' }
else {
tempvar ll ul cl
local ll `2'
local ul `3'
local cl `4'
}
* input error traps
if "`ci'" != "" & "`vari'" != "" {
di _n in re "Error: options 'ci' and 'var' cannot " _c
di in re "be specified together."
exit
}
if "`ci'" == "ci" & "`ul'" != "" {
di _n in bl "Note: option 'ci' specified."
}
if "`ci'" == "ci" & "`ul'" == "" {
di _n in re "Error: option 'ci' specified but varlist " _c
di in re "has only 2 variables."
exit
}
if "`ci'" != "ci" & "`vari'" != "vari" & "`ul'" != "" {
di _n in bl "Warning: varlist has 3 variables but option " _c
di in bl "'ci' not specified; 'ci' assumed."
local ci "ci"
local vari ""
}
if "`vari'" == "vari" & "`ul'" != "" {
di _n in re "Error: option 'var' specified but varlist " _c
di in re "has more than 2 variables."
exit
}
if "`vari'" == "vari" & "`ul'" == "" {
di _n in bl "Note: option 'var' specified."
}
if "`vari'" != "vari" & "`ul'" == "" {
di _n in bl "Note: default data input format (theta, " _c
di in bl "se_theta) assumed."
}
* Select data to analyze
if "`ul'" == "" { markout `touse' `theta' `setheta' }
else { markout `touse' `theta' `ll' `ul' }
preserve /* Note: data preserved here */
* Generate `by' groups
if "`by'" != "" {
confirm var `by'
sort `by'
qui by `by': gen byte `byg' = _n==1
qui replace `byg' = sum(`byg')
local byn = `byg'[_N]
}
else {
qui gen byte `byg' = 1
local byn = 1
}
* Generate `by' labels -- if required
if ("`by'" != "") {
capture decode `by', gen(`bylabel')
if _rc != 0 {
local type : type `by'
if substr("`type'",1,3) != "str" {
qui gen str8 `bylabel' = string(`by')
}
else { qui gen `type' `bylabel' = `by' }
}
* v7 fix
encode `bylabel', gen(`bylabl')
}
* Do calculations
* initial calculations...
if "`vari'" == "vari" { qui replace `setheta' = sqrt(`setheta')}
if "`ci'" == "ci" {
capture confirm variable `cl'
if _rc~=0 { qui gen `zz' = invnorm(.975) }
else {
qui replace `cl' = `cl' * 100 if `cl' < 1
qui gen `zz' = -1 * invnorm((1- `cl' / 100) / 2 )
qui replace `zz' = invnorm(.025) if `zz'==.
}
qui gen `setheta' = ( ln(`ul') - ln(`ll')) / 2 / `zz'
qui replace `theta' = ln(`theta')
}
if "`if'" != "" { ifexp "`if'" }
if "`by'" != "" {
scalar `sk' = 0
scalar `sks' = 0
scalar `svks' = 0
scalar `sbv' = 0
scalar `sv' = 0
}
* loop through by-values
local j = 1
while `j' <= `byn' { /* start of loop for each `by' group */
summ `touse' if `touse' & `byg' == `j', meanonly
local data = _result(1)
* Calculate stats
qui {
gen `var' = `setheta'^2
gen `w' = 1/`var'
egen `sw' = sum(`w') if `touse' & `byg' == `j'
gen `vt' = `var' - 1 / `sw'
gen `wtheta' = `w' * `theta'
egen `swtheta' = sum(`wtheta') if `touse' & `byg' == `j'
gen `Ts' = (`theta' - `swtheta' / `sw') / sqrt(`vt')
gen `wl' = `w' * `theta'
egen `swl' = sum(`wl') if `touse' & `byg' == `j'
gen `RRm' = `swl' / `sw'
scalar `oe' = `RRm'
}
qui capture ktau2 `var' `Ts' if `touse' & `byg' == `j'
if _rc == 0 {
scalar `k' = $S_1
scalar `ks' = $S_4
scalar `sdks' = $S_5
scalar `p' = $S_6
scalar `zu' = $S_7
scalar `pcc' = $S_8
scalar `zcc' = $S_9
scalar `c' = $S_10
}
else if _rc == 2001 {
scalar `k' = `data'
scalar `ks' = .
scalar `sdks' = .
scalar `p' = .
scalar `zu' = .
scalar `pcc' = .
scalar `zcc' = .
scalar `c' = .
}
else {
di in re "error " _rc " in call to ktau2"
exit
}
if "`by'" != "" & `k' > 1 {
scalar `sk' = `sk' + `k'
scalar `sks' = `sks' + `ks'
scalar `svks' = `svks' + `sdks'^2
}
* Egger's bias test
tempvar prec snd
qui gen `prec'= 1 / `setheta'
qui gen `snd' = `theta' / `setheta'
qui regr `snd' `prec' if `touse' & `byg' == `j'
capture matrix b = get(_b)
if _rc == 0 {
local df = e(N) - 2
local bias = b[1,2]
capture matrix V = get(VCE)
if _rc == 0 {
local pb = tprob(`df', b[1,2] / sqrt(V[2,2]))
* 1.1.3 fix
* if "`by'" != "" {
if "`by'" != "" & V[2,2] != 0 & `data' > 0 {
scalar `sbv' = `sbv' + `bias' / V[2,2]
scalar `sv' = `sv' + 1 / V[2,2]
}
}
}
else {
local bias = .
local pb = .
}
* Display results
if "`by'" != "" {
* use this display if a by_var was specified...
* .....display output header
if `j' == 1 {
di " "
di in gr "Tests for Publication Bias"
di " "
local sp = 8 - length("`by'")
#delimit ;
di in gr "-------------------------------------------------"
"------------------------------" ;
di in gr " | | Begg's Begg's"
" cont. corr. | Egger's " ;
di in gr _skip(`sp') "`by' | n | score s.d. z"
" p z p | bias p" ;
di in gr "---------+------+--------------------------------"
"---------------+--------------" ;
#delimit cr
local scs " "
}
* v7 fix
* local sp = 8 - length(`bylabel')
local blab: label (`bylabl') `j'
local sp = 8 - length("`blab'")
if `c' == 1 {
local cs "*"
local scs "*"
}
else {local cs " "}
* .....display results for each by-value
if `data' > 0 {
* v7 fix
di in gr _skip(`sp') "`blab' | " in ye %4.0f `k' _c
* di in gr _skip(`sp') `bylabel' " | " in ye %4.0f `k' _c
di in gr " |" in ye %4.0f `ks' in gr "`cs' " _c
di in ye %6.3f `sdks' " " %6.2f `zu' " " %6.3f `p' " " _c
di in ye %6.2f `zcc' " " %6.3f `pcc' in gr " |" _c
di in ye %6.2f `bias' " " %6.3f `pb'
}
* .....do stratified calculations
if `j' == `byn' {
scalar `zu' = `sks' / sqrt(`svks')
scalar `p' = 2 * (1 - normprob(abs(`zu')))
scalar `zcc' = sign(`sks')*(abs(`sks') - 1) / sqrt(`svks')
scalar `pcc' = 2 * (1 - normprob(abs(`zcc')))
scalar `k' = `sk'
scalar `ks' = `sks'
scalar `sdks' = sqrt(`svks')
drop `sw' `wl' `swl' `RRm'
qui egen `sw' = sum(`w') if `touse'
qui gen `wl' = `w' * `theta'
qui egen `swl' = sum(`wl') if `touse'
qui gen `RRm' = `swl' / `sw'
scalar `oe' = `RRm'
qui regr `snd' `prec' if `touse'
capture matrix b = get(_b)
if _rc == 0 {
matrix V = get(VCE)
local df = e(N) - 2
local bias = b[1,2]
local pb = tprob(`df', b[1,2]/sqrt(V[2,2]))
}
else {
local bias = .
local pb = .
}
local bias = `sbv' / `sv'
local pb = 1 - normprob(`bias' / sqrt(1 / `sv'))
* .....and display overall (stratified) results
di in gr "---------+------+----------------------------" _c
di in gr "-------------------+--------------"
di in gr " overall | " in ye %4.0f `sk' _c
di in gr " |" in ye %4.0f `sks' in gr "`scs' " _c
di in ye %6.3f sqrt(`svks') " " %6.2f `zu' " " %6.3f `p' _c
di in ye " " %6.2f `zcc' " " %6.3f `pcc' in gr " |" _c
di in ye %6.2f `bias' " " %6.3f `pb'
di in gr "---------------------------------------------" _c
di in gr "----------------------------------"
if "`scs'" == "*" {
di in gr _skip(21) "`scs' (corrected for ties)"
}
}
}
else {
* use this display if no by_var was specified...
* Begg's
di _n in gr "Tests for Publication Bias"
di _n in gr "Begg's Test"
di " "
di in gr " adj. Kendall's Score (P-Q) = " in ye %7.0f `ks'
di _c in gr " Std. Dev. of Score = " in ye %7.2f `sdks'
if `c' == 1 { di in gr " (corrected for ties)" }
else { di " " }
di in gr " Number of Studies = " in ye %7.0f `k'
di in gr " z = " in ye %7.2f `zu'
di in gr " Pr > |z| = " in ye %7.3f `p'
di _c in gr " z = " in ye %7.2f `zcc'
di in gr " (continuity corrected)"
di _c in gr " Pr > |z| = " in ye %7.3f `pcc'
di in gr " (continuity corrected)"
* Egger's
tempvar prec snd
qui gen `prec'= 1 / `setheta'
qui gen `snd' = `theta' / `setheta'
qui regr `snd' `prec' if `touse'
capture matrix b = get(_b)
if _rc == 0 {
matrix V = get(VCE)
local obs = e(N)
local df = `obs' - 2
matrix colnames b = slope bias
matrix rownames V = slope bias
matrix colnames V = slope bias
matrix post b V, dep(Std_Eff) dof(`df') obs(`obs')
di _n in gr "Egger's test"
matrix mlout, level(`level')
}
else {
di _n in gr "Egger's Test" _n
di in ye " - undefined for only 1 study"
}
}
cap drop `var' `w' `sw' `vt' `wtheta' `swtheta'
cap drop `Ts' `wl' `swl' `RRm'
local j = `j' + 1
} /* end of loop for each `by' group */
* Graph a bias plot
local g = lower(substr("`graph'",1,1))
if "`g'" == "b" {
beggph `theta' `setheta' `touse', level(`level') `ci' `options'
}
if "`g'" == "e" {
egggph `theta' `setheta' `touse', level(`level') `options'
}
* Save globals
global S_1 = `k'
global S_2 = `ks'
global S_3 = `sdks'
global S_4 = `p'
global S_5 = `pcc'
global S_6 = `bias'
global S_7 = `pb'
global S_8 = `oe'
* return globals
return scalar k = `k'
return scalar score = `ks'
return scalar score_sd = `sdks'
return scalar Begg_p = `p'
return scalar Begg_pcc = `pcc'
return scalar Egger_bc = `bias'
return scalar Egger_p = `pb'
return scalar effect = `oe'
exit
end
* ***************************************************
program define beggph
version 6.0
* creates the Begg funnel plot graph
* Setup
syntax varlist(min=3 max=3) [if] [in] [, CI L1title(str) /*
*/ L2title(str) Connect(str) Symbol(str) SOrt Pen(str) /*
*/ T2title(str) B2title(str) YLAbel(str) XLAbel(str) /*
*/ LEVel(integer $S_level) GAp(str) GWeight * ]
tokenize `varlist'
tempvar touse theta setheta
local theta `1'
local setheta `2'
local touse `3'
preserve
* Graph options
if "`connect'" == "" { local connect "lll." }
else {
local lll = length("`connect'")
if `lll' == 1 { local connect "`connect'll." }
else if `lll' == 2 { local connect "`connect'l." }
else if `lll' == 3 { local connect "`connect'." }
}
local connect "co(`connect')"
if "`symbol'" == "" { local symbol "iiio" }
else {
local lll = length("`symbol'")
if `lll' == 1 { local symbol "`symbol'iio" }
else if `lll' == 2 { local symbol "`symbol'io" }
else if `lll' == 3 { local symbol "`symbol'o" }
}
local symbol "sy(`symbol')"
if "`pen'" == "" { local pen "3552" }
else {
local lll = length("`pen'")
if `lll' == 1 { local pen "`pen'552" }
else if `lll' == 2 {
local pen = "`pen'" + substr("`pen'",2,1) + "2"
}
else if `lll' == 3 { local pen "`pen'2" }
}
local pen "pen(`pen')"
if "`l2title'" == "" {
local l2title : variable label `theta'
if "`l2title'" == "" { local l2title "`theta'" }
}
if "`ci'" == "" { local l2title "l2(`l2title')" }
else { local l2title "l2(log[`l2title'])" }
if "`l1title'" == "" { local l1title "" "" }
local l1title "l1(`l1title')"
if "`b2title'" == "" {
local b2title : variable label `theta'
if "`b2title'" == "" { local b2title "`theta'" }
}
if "`ci'" == "" { local b2title = "b2(s.e. of: `b2title')" }
else { local b2title = "b2(s.e. of: log[`b2title'])" }
if `"`t2title'"' == `""' {
local t2title = "Begg's funnel plot with pseudo"
local t2title = "`t2title' `level'% confidence limits"
local t2title "t2(`"`t2title'"')"
}
else if `"`t2title'"' == `"."' { local t2title }
else { local t2title "t2(`"`t2title'"')" }
if "`xlabel'" == "" { local xlabel "xla" }
else { local xlabel "xlabel(`xlabel')" }
if "`ylabel'" == "" { local ylabel "yla" }
else { local ylabel "ylabel(`ylabel')" }
if "`sort'" == "" { local sort "sort" }
if "`gap'" == "" { local gap "gap(3)" }
else { local gap "gap(`gap')" }
tempvar ll2 ul2 z mmm var w sw wl swl RRm
tempname oe
qui {
if `level' < 1 { local `level' =`level' * 100 }
local z = -1 * invnorm((1 - `level' / 100) / 2)
local obs1=_N+1
set obs `obs1'
replace `setheta'=0 in `obs1'
replace `theta' = . in `obs1'
gen `var' = `setheta'^2
gen `w' = 1/`var'
egen `sw' = sum(`w') if `touse'
gen `wl' = `w' * `theta'
egen `swl' = sum(`wl') if `touse'
gen `RRm' = `swl' / `sw'
scalar `oe' = `RRm'
egen `mmm' = min(`RRm')
replace `RRm' = `mmm' if `setheta' == 0
gen `ll2' = `RRm' - `z' * `setheta'
gen `ul2' = `RRm' + `z' * `setheta'
}
if "`gweight'" != "" {
tempvar ww
qui gen `ww' = `w'
qui replace `ww' = 1 if `setheta' == 0
local gw "[w=`ww']"
}
else { local gw "" }
#delimit ;
graph `RRm' `ll2' `ul2' `theta' `setheta' `gw' if `touse',
`connect' `symbol' `t2title' `l2title' `b2title'
`l1title' `xlabel' `ylabel' `sort' `pen' `gap' `options';
#delimit cr
exit
end
* ***************************************************
program define egggph
version 6.0
* creates the Egger regression asymmetry plot graph
* Setup
syntax varlist(min=3 max=3) [if] [in] [, LEVel(integer 95) GWeight /*
*/ GAp(str) Connect(str) Symbol(str) SOrt Pen(str) T2title(str) /*
*/ B2title(str) YLAbel(str) XLAbel(str) L1title(str) L2title(str) *]
tokenize `varlist'
tempvar touse theta setheta
local theta `1'
local setheta `2'
local touse `3'
preserve
* Graph options
if "`connect'" == "" { local connect ".ll" }
else {
local lll = length("`connect'")
if `lll' == 1 { local connect "`connect'll" }
else if `lll' == 2 { local connect "`connect'l" }
}
local connect "co(`connect')"
if "`symbol'" == "" { local symbol "oid" }
else {
local lll = length("`symbol'")
if `lll' == 1 { local symbol "`symbol'id" }
else if `lll' == 2 { local symbol "`symbol'd" }
}
local symbol "sy(`symbol')"
if "`pen'" == "" { local pen "233" }
else {
local lll = length("`pen'")
if `lll' == 1 { local pen "`pen'33" }
else if `lll' == 2 {local pen = "`pen'" + substr("`pen'",2,1)}
}
local pen "pen(`pen')"
if "`l2title'" == "" { local l2title "standardized effect" }
local l2title "l2(`l2title')"
if "`l1title'" == "" { local l1title "" "" }
local l1title "l1(`l1title')"
if "`b2title'" == "" { local b2title "precision" }
local b2title = "b2(`b2title')"
if `"`t2title'"' == `""' {
local t2title = "Egger's publication bias plot"
local t2title "t2(`"`t2title'"')"
}
else if `"`t2title'"' == `"."' { local t2title t2(" ")}
else { local t2title "t2(`"`t2title'"')" }
if "`xlabel'" == "" { local xlabel "xla" }
else { local xlabel "xlabel(`xlabel')" }
if "`ylabel'" == "" { local ylabel "yla" }
else { local ylabel "ylabel(`ylabel')" }
if "`sort'" == "" { local sort "sort" }
if "`gap'" == "" { local gap "gap(3)" }
else { local gap "gap(`gap')" }
qui {
local obs1 = _N + 1
local obs2 = _N + 2
local obs3 = _N + 3
set obs `obs3'
replace `setheta' = 0 in `obs1'/`obs3'
replace `theta' = . in `obs1'/`obs3'
tempvar prec snd
gen `prec' = 1 / `setheta' if `setheta' > 0
gen `snd' = `theta' / `setheta' if `setheta' > 0
replace `prec' = 0 if `prec' == .
regr `snd' `prec' if `touse'
tempvar reg ci
capture matrix b = get(_b)
if _rc == 0 {
matrix V = get(VCE)
local df = e(N) - 2
gen `reg' = b[1,2] + `prec' * b[1,1]
gen `ci' = .
#delimit ;
replace `ci' = b[1,2] - sqrt(V[2,2])
* invt(`df', `level'/100) in `obs2' ;
replace `ci' = b[1,2] + sqrt(V[2,2])
* invt(`df', `level'/100) in `obs3' ;
#delimit cr
}
else {
gen `reg' = .
gen `ci' = .
}
}
if "`gweight'" != "" {
tempvar ww
qui gen `ww' = 1 / `setheta'^2
qui replace `ww' = 1 if `setheta' == 0
local gw "[w=`ww']"
}
else { local gw "" }
#delimit ;
graph `snd' `reg' `ci' `prec' `gw' if `touse', yli(0) xli(0)
`connect' `symbol' `t2title' `l2title' `b2title'
`l1title' `xlabel' `ylabel' `sort' `pen' `gap' `options';
#delimit cr
exit
end
* ***************************************************
*! ktau2 version 4.1.0 26sep97 TJS
program define ktau2
version 4.0
* modification of ktau to allow N==2, un-continuity-corrected
* z and p values, and to pass more parameters
local varlist "req ex min(2) max(2)"
local if "opt"
local in "opt"
parse "`*'"
parse "`varlist'", parse(" ")
local x "`1'"
local y "`2'"
tempname k N NN pval score se tau_a tau_b
tempname xt xt2 xt3 yt yt2 yt3
tempvar doit nobs order work
mark `doit' `in' `if'
markout `doit' `x' `y'
quietly count if `doit'
scalar `N' = _result(1)
if `N' < 2 { error 2001 }
local Nmac = `N'
qui {
gen long `order' = _n /* restore ordering at end */
replace `doit' = -`doit'
sort `doit' /* put obs for computation first */
gen double `work' = 0 /* using type double is fastest */
scalar `k' = 2
while (`k' <= `N') {
local kk = `k' - 1
#delimit ;
replace `work' = `work'
+ sign((`x' - `x'[`k'])*(`y' - `y'[`k']))
in 1/`kk' ; /* using "in" is fastest */
#delimit cr
scalar `k' = `k' + 1
}
replace `work' = sum(`work') in 1/`Nmac'
scalar `score' = `work'[`N']
/* Calculate ties on `x' */
egen long `nobs' = count(`x') in 1/`Nmac', by(`x')
tempvar nobsxm
egen `nobsxm' = max(`nobs')
/* Calculate correction term for ties on `x' */
replace `work' = sum((`nobs' - 1)*(2*`nobs' + 5)) in 1/`Nmac'
scalar `xt' = `work'[`N']
/* Calculate correction term for pairs of ties on `x' */
replace `work' = sum(`nobs' - 1) in 1/`Nmac'
scalar `xt2' = `work'[`N']
/* Calculate correction term for triplets of ties on `x' */
replace `work' = sum((`nobs' - 1)*(`nobs' - 2)) in 1/`Nmac'
scalar `xt3' = `work'[`N']
/* Calculate ties on `y' */
drop `nobs'
egen long `nobs' = count(`y') in 1/`Nmac', by(`y')
tempvar nobsym
egen `nobsym' = max(`nobs')
/* Calculate correction term for ties on `y' */
replace `work' = sum((`nobs' - 1)*(2*`nobs' + 5)) in 1/`Nmac'
scalar `yt' = `work'[`N']
/* Calculate correction term for pairs of ties on `y' */
replace `work' = sum(`nobs' - 1) in 1/`Nmac'
scalar `yt2' = `work'[`N']
/* Calculate correction term for triplets of ties on `y' */
replace `work' = sum((`nobs' - 1)*(`nobs' - 2)) in 1/`Nmac'
scalar `yt3' = `work'[`N']
/* Compute Kendall's tau-a, tau-b, s.e. of score, and pval */
scalar `NN' = `N'*(`N' - 1)
scalar `tau_a' = 2*`score'/`NN'
scalar `tau_b' = 2*`score'/sqrt((`NN' - `xt2')*(`NN' - `yt2'))
#delimit ;
scalar `se' = `NN'*(2*`N' + 5);
tempname tmax;
scalar `tmax' = max(`nobsxm', `nobsym');
if `tmax' > 1 { scalar `se' = `se'
- (`xt' - `yt')
+ `xt3'*`yt3'/(9*`NN'*(`N' - 2))
+ `xt2'*`yt2'/(2*`NN') } ;
scalar `se' = sqrt((1/18)*`se');
#delimit cr
local zcc = (abs(`score') - 1) / `se'
local z = `score '/ `se'
tempname pvalcc
if `score' == 0 {
scalar `pval' = 1
scalar `pvalcc' = 1
}
else scalar `pvalcc' = 2*(1 - normprob((abs(`score') - 1)/`se'))
else scalar `pval' = 2*(1 - normprob(abs(`score')/`se'))
/* Restore original ordering of data set */
sort `order'
}
/* Print results */
#delimit ;
di _n
in gr " Number of obs = " in ye %7.0f `N' _n
in gr "Kendall's tau-a = " in ye %12.4f `tau_a' _n
in gr "Kendall's tau-b = " in ye %12.4f `tau_b' _n
in gr "Kendall's score = " in ye %7.0f `score' _n
in gr " SE of score = " in ye %11.3f `se' _c ;
if `xt2' > 0 | `yt2' > 0 { di in gr " (corrected for ties)" _c } ;
di _n(2)
in gr "Test of Ho: `x' and `y' independent" _n
in gr " z = " in ye %12.2f `z' _n
in gr " Pr > |z| = " in ye %12.4f = `pval' _n(2)
in gr " z = " in ye %12.2f sign(`score')*`zcc' _n
in gr " Pr > |z| = " in ye %12.4f = `pvalcc'
in gr " (continuity corrected)" ;
#delimit cr
local c = 0
if `xt2' > 0 | `yt2' > 0 { local c = 1 }
global S_1 = `N'
global S_2 = `tau_a'
global S_3 = `tau_b'
global S_4 = `score'
global S_5 = `se'
global S_6 = `pval'
global S_7 = `z'
global S_8 = `pvalcc'
global S_9 = `zcc'
global S_10 = `c'
end
* ***************************************************
*! ifexp version 1.2.1 19nov98 TJS
program define ifexp
version 5.0
while substr("`1'",1,2) != "if" {
macro shift
}
local if "required"
local options "noVARlabel noVALlabel noUNabbrev noSPace Color(str)"
parse "`*'"
parse "`if'", parse(" ><=&|!()")
if "`color'" == "" {local color = "b"} /* set display color */
local color = lower(substr("`color'",1,1))
if index("wbgyr","`color'") == 0 {local color = "b"}
if "`if'" != "" {
local ifst " if " /* handle leading "if " */
local lif = 5
macro shift
if "`space'" != "nospace" { /* set up space */
local sp " "
local sp1 = 1
}
else { local sp1 = 0 }
di
while "`1'" != "" { /* start main loop on tokens */
capture confirm variable `1' /* var name ? */
local rc _rc
local m = 0
local n = length("`1'")
if `n' + `lif' > 78 { /* no room for token ? */
di in `color' "`ifst'"
local ifst "> "
local lif = 5
local n = 0
}
/* is punctuation */
if "`1'" == "==" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "<=" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == ">=" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "!=" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "<" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == ">" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "=" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "&" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "|" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "!" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == "(" { local ifst "`ifst'`1'`sp'" }
else if "`1'" == ")" { local ifst "`ifst'`1'`sp'" }
else if `rc' == 0 { /* is a variable name */
local tif "`1'"
if "`unabbrev'" != "nounabbrev" { /* allow unabbrev */
unabbrev `1'
local tif "$S_1"
}
if "`varlabel'" != "novarlabel" { /* allow var label ? */
local vrl : variable label `1'
if "`vrl'" != "" { local tif "`vrl'" } /* var label ? */
}
local wc: word count `tif' /* process > 1 word in label */
local w 1
while `w' < `wc' {
local wif: word `w' of `tif'
local wm = length("`wif'")
if `wm' + `lif' > 78 {
di in `color' "`ifst'"
local ifst "> "
local lif = 5
local n = `m'
local m = 0
}
else {
local lif = `lif' + `wm' + `sp1'
local ifst "`ifst'`wif'`sp'"
local w = `w' + 1
}
}
local tif: word `wc' of `tif' /* do last or only word */
local m = length("`tif'") /* no room for token ? */
if `m' + `lif' > 78 {
di in `color' "`ifst'"
local ifst "> "
local lif = 5
local n = `m'
local m = 0
}
local ifst "`ifst'`tif'`sp'"
local vll : value label `1'
}
else { /* is a value */
local tif "`1'" /* allow value label ? */
if "`vll'" != "" & "`vallabel'" != "novallabel" {
local lv : label `vll' `1'
if "`lv'" != "" { local tif "`lv'" } /* value label ? */
}
local wc: word count `tif' /* process > 1 word in label */
local w 1
while `w' < `wc' {
local wif: word `w' of `tif'
local wm = length("`wif'")
if `wm' + `lif' > 78 {
di in `color' "`ifst'"
local ifst "> "
local lif = 5
local n = `m'
local m = 0
}
else {
local lif = `lif' + `wm' + `sp1'
local ifst "`ifst'`wif'`sp'"
local w = `w' + 1
}
}
local tif: word `wc' of `tif' /* do last or only word */
local m = length("`tif'") /* no room for token ? */
if `m' + `lif' > 78 {
di in `color' "`ifst'"
local ifst "> "
local lif = 5
local n = `m'
local m = 0
}
local ifst "`ifst'`tif'`sp'"
}
/* set new line length */
if `m' > 0 { local lif = `lif' + `m' + `sp1' }
else { local lif = `lif' + `n' + `sp1' }
macro shift /* next token */
} /* end main loop on tokens */
di in `color' "`ifst'" /* display expanded if */
global S_1 "`ifst'"
exit
end

188
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/*
*! metabias dialog version 1.0.1, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metabias version 1.2.4, 08 Sep 2003, T. J. Steichen, steichen@triad.rr.com
Tests for publication bias in meta-analysis
-------------------------------------------
Syntax: metabias { theta { se_theta | var_theta } | exp(theta) ll ul [cl] }
[ if exp ] [ in range ] [, by(by_var) { var | ci }
graph({ begg | egger }) gweight level(#) graph_options ]
Install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "meta&bias (Publication Bias in Meta-analysis)" "db metabias"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metabias")
RESET res1, label("Reset")
DIALOG main, label("metabias 1.2.4 - Publication Bias in Meta-analysis") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 330 _ht5h, ///
label("Type of Data:")
RADIO r_se 15 25 100 ., ///
label("Theta, SE") first ///
onclickon(script main_se_on) ///
onclickoff(script main_se_off) ///
option(NONE)
RADIO r_var 115 25 100 ., ///
label("Theta, Var") middle ///
onclickon(script main_var_on) ///
onclickoff(script main_var_off) ///
option("var")
RADIO r_ci 215 25 110 ., ///
label("exp(Theta), CI") last ///
onclickon(script main_ci_on) ///
onclickoff(script main_ci_off) ///
option("ci")
TEXT tx_se 15 45 320 ., ///
label("Vars for theta, se(theta), in that order")
VARLIST vl_se @ _ss @ ., ///
label("Vars for theta, se(theta)")
TEXT tx_var @ 45 320 ., ///
label("Vars for theta, var(theta), in that order")
VARLIST vl_var @ _ss @ ., ///
label("Vars for theta, var(theta)")
TEXT tx_ci @ 45 320 ., ///
label("Vars for exp(theta), CI_LL, CI_UL, in that order")
VARLIST vl_ci @ _ss @ ., ///
label("Vars for exp(theta), CI_LL, CI_UL")
CHECKBOX cb_by 10 100 100 ., ///
label("By Variable:") ///
onclickon(main.vn_by.enable) ///
onclickoff(main.vn_by.disable)
VARNAME vn_by 110 100 230 ., ///
label("By Variable") ///
option("by")
GROUPBOX gb_graph 10 130 330 _ht3h, ///
label("Graph:")
RADIO r_none 15 150 100 ., ///
label("None") first ///
onclickon(main.cb_gwt.disable) ///
option(NONE)
RADIO r_begg 115 150 100 ., ///
label("Begg") middle ///
onclickon(main.cb_gwt.enable) ///
option("graph(begg)")
RADIO r_egger 215 150 100 ., ///
label("Egger") last ///
onclickon(main.cb_gwt.enable) ///
option("graph(egger)")
CHECKBOX cb_gwt 15 175 300 ., ///
label("Size Graph Symbol by Weights") ///
option("gweight")
END
INCLUDE ifin
SCRIPT main_se_on
BEGIN
main.tx_se.show
main.vl_se.show
main.tx_se.enable
main.vl_se.enable
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_se_off
BEGIN
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
END
SCRIPT main_var_on
BEGIN
main.tx_var.show
main.vl_var.show
main.tx_var.enable
main.vl_var.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_var_off
BEGIN
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_on
BEGIN
main.tx_ci.show
main.vl_ci.show
main.tx_ci.enable
main.vl_ci.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_off
BEGIN
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
PROGRAM command
BEGIN
put "metabias "
if main.r_se {
varlist main.vl_se
}
if main.r_var {
varlist main.vl_var
}
if main.r_ci {
varlist main.vl_ci
}
INCLUDE _ifin_pr
beginoptions
option radio(main r_se r_var r_ci)
optionarg main.vn_by
option radio(main r_none r_begg r_egger)
option main.cb_gwt
endoptions
END

211
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.-
help for ^metabias^ (STB-41: sbe19; STB-44: sbe19.1; STB-57: sbe19.2;
STB-58: sbe19.3; STB-61: sbe19.4)
.-
Tests for publication bias in meta-analysis
-------------------------------------------
^metabias^ { theta { se_theta | var_theta } | exp(theta) ll ul [cl] }
[ ^if^ exp ] [ ^in^ range ] [^, by(^by_var^)^ { ^v^ar | ^ci^ }
^g^raph^(b^egg | ^e^gger^) gw^eight ^l^evel^(^#^)^ graph_options ]
where { a | b |...} means choose one and only one of {a, b, ...}.
Description
-----------
^metabias^ performs the Begg and Mazumdar adjusted rank correlation test for
publication bias and performs the Egger, et al., regression asymmetry test for
publication bias. As options, it provides a funnel graph of the data or the
regression asymmetry plot.
The Begg adjusted rank correlation test is a direct statistical analogue of
the visual funnel graph. Note that both the test and the funnel graph have
low power for detecting publication bias. The Begg and Mazumdar procedure
tests for publication bias by determining if there is a significant
correlation between the effect estimates and their variances. ^metabias^
carries out this test by, first, standardizing the effect estimates to
stabilize the variances and, second, performing an adjusted rank correlation
test based on Kendall's tau.
The Egger, et al., regression asymmetry test and the regression asymmetry plot
tend to suggest the presence of publication bias more frequently than the Begg
approach. The Egger test detects funnel plot asymmetry by determining whether
the intercept deviates significantly from zero in a regression of the
standardized effect estimates against their precision.
Egger, et al., claim that the test predicts the discordance (if any) of
meta-analytic results and single large trials, but no formal analysis of
coverage (i.e., nominal significance level) or power has been performed.
The user provides the effect estimate, ^theta^, to ^metabias^ as a log
risk ratio, log odds ratio, or other direct measure of effect. Along
with theta, the user supplies a measure of theta's variability (i.e.,
its standard error, ^se_theta^, or its variance, ^var_theta^).
Alternatively, the user may provide the exponentiated form,
^exp(theta)^, (i.e., a risk ratio or odds ratio) and its confidence
interval, ^(ll, ul)^.
The funnel graph plots ^theta^ versus ^se_theta^. Guide lines to assist in
visualizing the funnel are plotted at the variance-weighted (fixed effects)
meta-analytic effect estimate and at pseudo confidence interval limits about
that effect estimate (i.e., at ^theta +/- z * se_theta^, where ^z^ is the
standard Normal variate for the confidence level specified by option ^level()^.
Asymmetry on the right of the graph (where studies with high standard error
are plotted) may give evidence of publication bias.
The regression asymmetry graph plots the standardized effect estimates,
^theta / se_theta^, versus precision, ^1 / se_theta^, along with the
regression line and the confidence interval about the intercept. Failure of
this confidence interval to include zero indicates asymmetry in the funnel
plot and may give evidence of publication bias. Guide lines at x = 0 and
y = 0 are plotted to assist in visually determining if zero is in the
confidence interval.
^metabias^ will perform stratified versions of both the Begg and Mazumdar test
and the Egger regression asymmetry test when option ^by(by_var)^ is specified.
Variable ^by_var^ indicates the categorical variable that defines the strata.
The procedure reports results for each strata and for the stratified tests.
The graphs, if selected, plot only the combined unstratified data.
Options
-------
^by(by_var)^ requests that the stratified tests be carried out with
strata defined by ^by_var^.
^var^ indicates that ^var_theta^ was supplied on the command line
instead of ^se_theta^. Option ^ci^ should not be specified when
option ^var^ is specified.
^ci^ indicates that ^exp(theta)^ and its confidence interval, ^(ll,
ul)^, were supplied on the command line instead of ^theta^ and
^se_theta^. Option ^var^ should not be specified when option ^ci^ is
specified.
^graph(begg)^ requests the Begg funnel graph showing the data, the
fixed-effects (variance-weighted) meta-analytic effect, and the pseudo
confidence interval limits about the meta-analytic effect.
^graph(egger)^ requests the Egger regression asymmetry plot showing the
standardized effect estimates versus precision, the regression line, and
the confidence interval about the intercept.
^gweight^ requests that the graphic symbols representing the data in the
plot be sized proportional to the inverse variance.
^level()^ sets the confidence level % for the pseudo confidence intervals;
the default is 95%.
^graph_options^ are those allowed with ^graph, twoway^. For
^graph(begg)^, the default graph_options include ^connect(lll.)^,
^symbol(iiio)^, and ^pen(3552)^ for displaying the meta-analytic
effect, the pseudo confidence interval limits (two lines), and the
data points, respectively. For ^graph(egger)^, the default
graph_options include ^connect(.ll)^, ^symbol(oid)^, and ^pen(233)^
for displaying the data points, regression line, and the confidence
interval about the intercept, respectively. Setting ^t2title(.)^
blanks out the default ^t2title^ in either graph.
Required input variables
------------------------
^theta^ the effect estimate
^se_theta^ the corresponding standard error
or
^theta^ the effect estimate
^var_theta^ the corresponding variance
or
^exp(theta)^ the risk (or odds) ratio
^ll^ the lower limit of the risk ratio's confidence interval
^ul^ the upper limit of the risk ratio's confidence interval
[^cl^] optional (see below)
Optional input variable
-----------------------
^cl^ contains the confidence level of the confidence interval defined by ^ll^
and ^ul^. If ^cl^ is not provided, the procedure assumes that each confidence
interval is at the 95% confidence level. ^cl^ allows the user to provide the
confidence level, by study, when the confidence interval is not at the default
level. ^cl^ can be specified with or without a decimal point. For example,
90 and .90 are equivalent and may be mixed (i.e., 90, .95, 80, .90 etc.).
Note
----
If your data are in raw count format, program ^metan^ can be used to
facilitate conversion to effect format. ^metan^ automatically adds
^exp(theta)^ and ^se_theta^ variables to the dataset, calling them
^_ES^ and ^_seES^. You must manually generate ^theta^ as the natural
log of ^_ES^ (for example, ^gen _lnES = ln(_ES)^) then input the
effect-format variables, ^_lnES^ and ^_seES^, using ^metabias^'s
default input method.
Saved values
------------
The following items are saved in the global ^S_^# macros and are returned in ^r()^.
^S_1 r(k)^ number of studies
^S_2 r(score)^ Begg's score
^S_3 r(score_sd)^ s.d. of Begg's score
^S_4 r(Begg_p)^ Begg's p value
^S_5 r(Begg_pcc)^ Begg's p, continuity corrected
^S_6 r(Egger_bc)^ Egger's bias coefficient
^S_7 r(Egger_p)^ Egger's p value
^S_8 r(effect)^ overall effect (log scale)
Examples
--------
. ^metabias logrr selogrr, graph(begg)^
. ^metabias logrr varlogrr if site==3, var graph(egger)^
. ^metabias rr ll ul, ci by(site)^
. ^metabias logor selogor if region==4, graph(egger) level(90)^
Note
----
^metabias^ calls program ^ktau2^, a modification of the ^ktau^ program
supplied with Stata. ^ktau2^ is included in the distribution files
for this version of ^metabias^.
References
----------
Begg, C. B., Mazumdar, M., 1994. Operating characteristics of a rank
correlation test for publication bias. Biometrics 50: 1088-1101.
Egger, M., Smith, G. D., Schneider, M., Minder, C., 1997. Bias in
meta-analysis detected by a simple, graphical test. British Medical
Journal 315: 629-634.
Author
------
Thomas J. Steichen, RJRT, steicht@@rjrt.com
Also see
--------
STB: STB-41 sbe19; STB-44 sbe19.1
Manual: [R] spearman
On-line: help for @meta@, @metan@, and @ktau@ (if installed)

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/*
*! metacum dialog version 1.0.1, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metacum version 1.02, Jan 1998, Jonathan Sterne, jonathan.sterne@bristol.ac.uk
metacum VERSION 1.02 jan1998
Cumulative meta-analysis, with graphics
---------------------------------------
Syntax: metacum { theta | exp(theta) } { se_theta | var_theta | ll ul [cl] }
[if exp] [in range] effect(f|r) [ , id(strvar) var ci eform level(#)
graph cline ltrunc(#) rtrunc(#) csize(#) fmult(#) ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Cumulative meta-analysis (meta&cum)" "db metacum"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metacum")
RESET res1, label("Reset")
DIALOG main, label("metacum 1.02 - Cumulative Meta-analysis") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 330 _ht5h, ///
label("Type of Data:")
RADIO r_se 15 25 100 ., ///
label("Theta, SE") first ///
onclickon(script main_se_on) ///
onclickoff(script main_se_off) ///
option(NONE)
RADIO r_var 115 25 100 ., ///
label("Theta, Var") middle ///
onclickon(script main_var_on) ///
onclickoff(script main_var_off) ///
option("var")
RADIO r_ci 215 25 110 ., ///
label("exp(Theta), CI") last ///
onclickon(script main_ci_on) ///
onclickoff(script main_ci_off) ///
option("ci")
TEXT tx_se 15 45 320 ., ///
label("Vars for theta, se(theta), in that order")
VARLIST vl_se @ _ss @ ., ///
label("Vars for theta, se(theta)")
TEXT tx_var @ 45 320 ., ///
label("Vars for theta, var(theta), in that order")
VARLIST vl_var @ _ss @ ., ///
label("Vars for theta, var(theta)")
TEXT tx_ci @ 45 320 ., ///
label("Vars for exp(theta), CI_LL, CI_UL, in that order")
VARLIST vl_ci @ _ss @ ., ///
label("Vars for exp(theta), CI_LL, CI_UL")
CHECKBOX cb_id 10 100 100 ., ///
label("ID Variable:") ///
onclickon(main.vn_id.enable) ///
onclickoff(main.vn_id.disable)
VARNAME vn_id 110 100 230 ., ///
label("ID Variable") ///
option("id")
GROUPBOX gb_effect 10 130 110 _ht3h, ///
label("Pooling Model:")
RADIO r_fixed 15 145 65 ., ///
label("Fixed") first ///
option("effect(f)")
RADIO r_random 15 165 75 ., ///
label("Random") last ///
option("effect(r)")
CHECKBOX cb_level 160 145 75 ., ///
label("CI Level:") ///
onclickon(main.ed_level.enable) ///
onclickoff(main.ed_level.disable)
EDIT ed_level 240 @ 40 ., ///
label("Level") ///
numonly default(95) ///
option("level")
CHECKBOX cb_eform 160 165 120 ., ///
label("Use exp(theta)") ///
option("eform")
END
DIALOG graph, tabtitle("Graph")
BEGIN
CHECKBOX cb_graph 10 10 330 _ht7h, ///
groupbox ///
onclickon(program graph_on) ///
onclickoff(program graph_off) ///
label("Draw Graph:") ///
option("graph")
CHECKBOX cb_cline 20 35 150 ., ///
label("Draw Estimate line") ///
option("cline")
CHECKBOX cb_ltrunc 20 60 95 ., ///
label("Left truncate:") ///
onclickon(graph.ed_ltrunc.enable) ///
onclickoff(graph.ed_ltrunc.disable)
EDIT ed_ltrunc 130 @ 40 ., ///
label("Left truncate") numonly ///
option("ltrunc")
CHECKBOX cb_rtrunc 20 80 105 ., ///
label("Right truncate:") ///
onclickon(graph.ed_rtrunc.enable) ///
onclickoff(graph.ed_rtrunc.disable)
EDIT ed_rtrunc 130 @ 40 ., ///
label("Right truncate") numonly ///
option("rtrunc")
CHECKBOX cb_fmult 195 60 95 ., ///
label("Font scale:") ///
onclickon(graph.ed_fmult.enable) ///
onclickoff(graph.ed_fmult.disable)
EDIT ed_fmult 290 60 40 ., ///
label("Font scale") ///
numonly default("1") ///
option("fmult")
CHECKBOX cb_csize 195 80 95 ., ///
label("Circle size:") ///
onclickon(graph.ed_csize.enable) ///
onclickoff(graph.ed_csize.disable)
EDIT ed_csize 290 80 40 ., ///
label("Circle size") ///
numonly default("180") ///
option("csize")
GROUPBOX gb_gopts7 10 145 330 _ht2h, ///
label("Allowed Graph7 Options:")
EDIT ed_gopts7 15 165 320 ., ///
label("Graph7 Options")
END
INCLUDE ifin
SCRIPT main_se_on
BEGIN
main.tx_se.show
main.vl_se.show
main.tx_se.enable
main.vl_se.enable
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_se_off
BEGIN
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
END
SCRIPT main_var_on
BEGIN
main.tx_var.show
main.vl_var.show
main.tx_var.enable
main.vl_var.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_var_off
BEGIN
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_on
BEGIN
main.tx_ci.show
main.vl_ci.show
main.tx_ci.enable
main.vl_ci.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_off
BEGIN
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
PROGRAM graph_on
BEGIN
call graph.cb_cline.enable
call graph.cb_fmult.enable
call graph.cb_ltrunc.enable
call graph.cb_rtrunc.enable
call graph.cb_csize.enable
call graph.gb_gopts7.enable
call graph.ed_gopts7.enable
if graph.cb_fmult {
call graph.ed_fmult.enable
}
if graph.cb_ltrunc {
call graph.ed_ltrunc.enable
}
if graph.cb_rtrunc {
call graph.ed_rtrunc.enable
}
if graph.cb_csize {
call graph.ed_csize.enable
}
END
PROGRAM graph_off
BEGIN
call graph.cb_cline.disable
call graph.cb_fmult.disable
call graph.cb_ltrunc.disable
call graph.cb_rtrunc.disable
call graph.cb_csize.disable
call graph.gb_gopts7.disable
call graph.ed_gopts7.disable
if graph.cb_fmult {
call graph.ed_fmult.disable
}
if graph.cb_ltrunc {
call graph.ed_ltrunc.disable
}
if graph.cb_rtrunc {
call graph.ed_rtrunc.disable
}
if graph.cb_csize {
call graph.ed_csize.disable
}
END
PROGRAM command
BEGIN
put "metacum "
if main.r_se {
varlist main.vl_se
}
if main.r_var {
varlist main.vl_var
}
if main.r_ci {
varlist main.vl_ci
}
INCLUDE _ifin_pr
beginoptions
option radio(main r_se r_var r_ci)
optionarg main.vn_id
option radio(main r_fixed r_random)
option main.cb_eform
optionarg main.ed_level
option graph.cb_graph
option graph.cb_cline
optionarg graph.ed_ltrunc
optionarg graph.ed_rtrunc
optionarg graph.ed_fmult
optionarg graph.ed_csize
put graph.ed_gopts7
endoptions
END

349
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*! Version 1.0.2, JACS, 18 August 2003
program define metafunnel
version 8.1
* Graphs funnel plot, with standard error on the vertical axis
if ("`*'" == "") {
di "Syntax is:"
di in wh "metafunnel " in gr "{ theta { se | var } | " _c
di in gr "exp(theta) | ll ul [cl] } [" _c
di in wh "if " in gr "exp] [" in wh "in " in gr "range]"
di in gr " [ " in wh ", by(" in gr "by_var"in wh ")" _c
di in gr " { " in wh "v" in gr "ar | " in wh "ci" in gr " } " _c
di in wh "nol" in gr "ines " in wh "forc" in gr "enull " _c
di in wh "rev" in gr "erse " in wh "ef" in gr "orm"
di in gr " graph_options ]"
exit
}
syntax varlist(numeric min=2 max=4) [if] [in], [ by(varname) ///
Var CI SUbtitle(str) NOLines FORCenull REVerse EForm ///
XTitle(string) YTitle(string) XScale(string) YScale(string) ///
MSymbol(string) * ]
tempvar touse theta setheta etheta zz
tempvar ll2 ul2 vl z mmm vvar w sw wl swl RRm orign
tempname oe
tokenize `varlist'
local theta `1'
if "`3'" == "" {
local setheta `2'
}
else {
tempvar ll ul cl
local ll `2'
local ul `3'
local cl `4'
}
* input error traps
if "`ci'" != "" & "`var'" != "" {
di _n as error "Error: options 'ci' and 'var' cannot " _c
di as error "be specified together."
exit
}
if "`ci'" == "ci" & "`ul'" != "" {
di _n as text "Note: option 'ci' specified."
}
if "`ci'" == "ci" & "`ul'" == "" {
di _n as error "Error: option 'ci' specified but varlist " _c
di as error "has only 2 variables."
exit
}
if "`ci'" != "ci" & "`var'" != "var" & "`ul'" != "" {
di _n as text "Warning: varlist has 3 variables but option " _c
di as text "'ci' not specified; 'ci' assumed."
local ci "ci"
local var ""
}
if "`var'" == "var" & "`ul'" != "" {
di _n as error "Error: option 'var' specified but varlist " _c
di as error "has more than 2 variables."
exit
}
if "`var'" == "var" & "`ul'" == "" {
di _n as text "Note: option 'var' specified."
}
if "`var'" != "var" & "`ul'" == "" {
di _n as text "Note: default data input format (theta, " _c
di as text "se_theta) assumed."
}
* Select data to analyze
mark `touse' `if' `in'
if "`ul'" == "" {
markout `touse' `theta' `setheta'
}
else {
markout `touse' `theta' `ll' `ul'
}
preserve
quietly keep if `touse'
quietly count
if r(N)==0 {
di as error "No observations with nonmissing values of `by'"
exit 999
}
* initial calculations...
if "`var'" == "var" {
qui replace `setheta' = sqrt(`setheta')
}
if "`ci'" == "ci" {
di _n as text "Warning: ci option assumes that ratio measures are being used"
capture confirm variable `cl'
if _rc~=0 {
qui gen `zz' = invnorm(.975)
}
else {
qui replace `cl' = `cl' * 100 if `cl' < 1
qui gen `zz' = -1 * invnorm((1- `cl' / 100) / 2 )
qui replace `zz' = invnorm(.025) if `zz'==.
}
qui gen `setheta' = ( ln(`ul') - ln(`ll')) / 2 / `zz'
qui replace `theta' = ln(`theta')
}
* Graph options
if "`xtitle'" == "" {
local xti : variable label `theta'
if "`xti'" == "" {
local xti "`theta'"
}
}
else if "`xtitle'" ~= "" {
local xti "`xtitle'"
}
if "`ytitle'" == "" {
local yti : variable label `setheta'
if "`yti'" == "" {
local yti "s.e. of `theta'"
}
}
else if "`ytitle'" ~= "" {
local yti "`ytitle'"
}
capture assert "`ysca'"==""
if _rc~=0 {
display as error "ysca option not permitted"
exit 999
}
if "`yscale'"~="" {
local chkrev=index("`yscale'","rev")
display "chkrev: `chkrev'"
if `chkrev'~=0 & "`reverse'"=="" {
local ysca "`yscale'"
}
if `chkrev'==0 & "`reverse'"=="" {
local ysca "`yscale' reverse"
}
if `chkrev'~=0 & "`reverse'"~="" {
display "Parsing yscale: ,`yscale' "
tokenize `yscale'
while "`1'"~="" {
if index("`1'","rev")==0 {
local ysca "`ysca' `1'"
}
mac shift
}
}
if `chkrev'==0 & "`reverse'"~="" {
local ysca "`yscale'"
}
}
if "`yscale'"=="" {
if "`reverse'"=="" {
local ysca "reverse"
}
if "`reverse'"~="" {
local ysca "noreverse"
}
}
if "`subtitle'" == "" {
local subtitle = "Funnel plot with pseudo 95% confidence limits"
}
else if "`subtitle'" == "." { /* "." means blank it out */
local subtitle "" ""
}
local subtitle "subtitle(`subtitle')"
if "`msymbol'"=="" {
local symopt "O T S D + X Oh Th Sh Dh o t s d x oh th sh dh p"
}
if "`msymbol'"~="" {
local symopt "`msymbol'"
}
local msymbol "msymbol(`symopt')"
qui {
gen `orign'=_n
gen `vvar' = `setheta'^2
gen `w' = 1/`vvar'
egen `sw' = sum(`w') if `touse'
gen `wl' = `w' * `theta'
egen `swl' = sum(`wl') if `touse'
sort `orign'
gen `RRm' = `swl' / `sw'
local rxl=`RRm'[1]
scalar `oe' = `RRm'
egen `mmm' = min(`RRm')
replace `RRm' = `mmm' if `setheta' == 0
if "`forcenull'"~="" {
local rxl=0
}
sort `orign'
local obs1=_N+1
local obs2=_N+2
local obs3=_N+3
local obs4=_N+4
local obs5=_N+5
local obs6=_N+6
set obs `obs6'
replace `orign'=_n
replace `theta'=`rxl' in `obs1'
replace `theta'=`rxl' in `obs3'
gen `ll2' = 0 in `obs1'
gen `ul2' = 0 in `obs3'
sort `orign'
qui summ `setheta'
local maxse=r(max)
replace `theta' = `rxl'-(1.96*`maxse') in `obs2'
replace `theta' = `rxl'+(1.96*`maxse') in `obs4'
replace `ll2' = `maxse' in `obs2'
replace `ul2' = `maxse' in `obs4'
gen `vl' = 0 in `obs5'
replace `vl' = `maxse' in `obs6'
replace `theta' = `rxl' in `obs5'
replace `theta' = `rxl' in `obs6'
label var `ll2' "Lower CI"
label var `ul2' "Lower CI"
label var `vl' "Pooled"
if "`forcenull'"~="" {
label var `vl' "No effect"
}
}
* list `setheta' `ll2' `ul2' `vl' `theta' `orign'
* display "RRm: `rxl'"
local funopt "yscale(`ysca') `subtitle' ytitle("`yti'") xtitle("`xti'")"
if "`by'"=="" {
local yvar "`setheta'"
local legopt "legend(off)"
}
if "`by'"~="" {
qui levels `by', local(bylev)
local lev: word count `bylev'
if `lev'>20 {
di as text "Note: distinct group markers available for only 19 groups"
}
sort `orign'
qui drop if `by'==.&_n<`obs1'
qui count if `by'~=.
if r(N)==0 {
di as error "No observations with nonmissing values of `by'"
exit 999
}
forvalues b=1/`lev' {
local bylab ""
local bygroup: word `b' of `bylev'
tempname bg`b'
qui gen `bg`b''=`setheta' if `by'==`bygroup'
local bylab: label (`by') `b'
if "`bylab"=="" {
label variable `bg`b'' "`by'=`b'"
}
if "`bylab"~="" {
label variable `bg`b'' "`bylab'"
}
}
local yvar "`bg1'-`bg`lev''"
} /* end by processing */
if "`nolines'"=="nolines"&"`eform'"=="" {
* display "clause 1"
twoway (scatter `yvar' `theta', `legopt' `msymbol') ///
if `touse', `funopt' `options'
}
if "`nolines'"==""&"`eform'"=="" {
* display "clause 2"
twoway (scatter `yvar' `theta', `legopt' `msymbol') ///
(line `ll2' `ul2' `vl' `theta', msymbol(none none none) ///
clcolor(black black black) clpat(dash dash solid) ///
clwidth(medium medium medium)) ///
if `touse', `funopt' `options'
}
if "`eform'"~="" {
gen `etheta'=exp(`theta')
if "`xtitle'"=="" {
local xti : variable label `theta'
if "`xti'" == "" {
local xti "exp(`theta'), log scale"
}
else if "`xti'" ~= "" {
local xti "exp(`xti'), log scale"
}
}
capture assert "`xsca'"==""
if _rc~=0 {
display as error "xsca option not permitted"
exit 999
}
if "`xscale'"~="" {
local chklog=index("`xscale'","log")
if `chklog'==0 {
local xsca "`xscale' log"
}
else if `chklog'~=0 {
local xsca "`xscale'"
}
}
else if "`xscale'"=="" {
local xsca "log"
}
if "`nolines'"=="nolines" {
* display "clause 3"
twoway (scatter `yvar' `etheta', `legopt' `msymbol') ///
if `touse', `funopt' xscale(`xsca') `options'
}
if "`nolines'"=="" {
* display "clause 4"
local rxl=exp(`rxl')
twoway (scatter `yvar' `etheta', `legopt' `msymbol') ///
(line `ll2' `ul2' `vl' `etheta', msymbol(none none none) ///
clcolor(black black black) clpat(dash dash solid) ///
clwidth(medium medium medium)) ///
if `touse', `funopt' xscale(`xsca') `options'
}
}
end

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/*
*! metafunnel dialog version 1.0.1, 20 Aug 2003, T. J. Steichen, steichen@triad.rr.com
*! for metafunnel version 1.0.2, 18 Aug 2003, J.A.C. Sterne, Jonathan.Sterne@bristol.ac.uk
Graphs funnel plot, with standard error on the vertical axis
------------------------------------------------------------
Syntax: metafunnel { theta {se|var} | exp(theta) ll ul [cl] } [ if exp ] [ in range ] [,
BY(varname) Var CI
SUbtitle(str) NOLines FORCEnull REVerse EForm
XTitle(string) YTitle(string) XScale(string)
YScale(string) MSymbol(string) * ]
Install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "metafunn&el (Funnel Graph, vertical)" "db metafunnel"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_medium
INCLUDE header
HELP hlp1, view("help metafunnel")
RESET res1, label("Reset")
DIALOG main, label("metafunnel 1.0.2 - Funnel Graph, vertical") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 390 _ht5h, ///
label("Type of Data:")
RADIO r_se 15 25 100 ., ///
label("Theta, SE") first ///
onclickon(script main_se_on) ///
onclickoff(script main_se_off) ///
option(NONE)
RADIO r_var 115 25 100 ., ///
label("Theta, Var") middle ///
onclickon(script main_var_on) ///
onclickoff(script main_var_off) ///
option("var")
RADIO r_ci 215 25 110 ., ///
label("exp(Theta), CI") last ///
onclickon(script main_ci_on) ///
onclickoff(script main_ci_off) ///
option("ci")
TEXT tx_se 15 45 380 ., ///
label("Vars for theta, se(theta), in that order")
VARLIST vl_se @ _ss @ ., ///
label("Vars for theta, se(theta)")
TEXT tx_var @ 45 380 ., ///
label("Vars for theta, var(theta), in that order")
VARLIST vl_var @ _ss @ ., ///
label("Vars for theta, var(theta)")
TEXT tx_ci @ 45 380 ., ///
label("Vars for exp(theta), CI_LL, CI_UL, in that order")
VARLIST vl_ci @ _ss @ ., ///
label("Vars for exp(theta), CI_LL, CI_UL")
CHECKBOX cb_by 10 100 100 ., ///
label("By Variable:") ///
onclickon(main.vn_by.enable) ///
onclickoff(main.vn_by.disable)
VARNAME vn_by 110 100 290 ., ///
label("By Variable") ///
option("by")
CHECKBOX ck_subtitle 10 130 55 ., ///
label("Title:") ///
onclickon(main.ed_subtitle.enable) ///
onclickoff(main.ed_subtitle.disable)
EDIT ed_subtitle 70 @ 330 ., ///
label("Title") ///
option("subtitle")
CHECKBOX ck_xtitle 10 155 55 ., ///
label("X: title") ///
onclickon(main.ed_xtitle.enable) ///
onclickoff(main.ed_xtitle.disable)
EDIT ed_xtitle 70 @ 160 ., ///
label("X title") ///
option("xtitle")
CHECKBOX ck_xscale 240 @ 60 ., ///
label("scale") ///
onclickon(main.ed_xscale.enable) ///
onclickoff(main.ed_xscale.disable)
EDIT ed_xscale 300 @ 100 ., ///
label("X scale") ///
option("xscale")
CHECKBOX ck_ytitle 10 175 55 ., ///
label("Y: title") ///
onclickon(main.ed_ytitle.enable) ///
onclickoff(main.ed_ytitle.disable)
EDIT ed_ytitle 70 @ 160 ., ///
label("Y title") ///
option("ytitle")
CHECKBOX ck_yscale 240 @ 60 ., ///
label("scale") ///
onclickon(main.ed_yscale.enable) ///
onclickoff(main.ed_yscale.disable)
EDIT ed_yscale 300 @ 100 ., ///
label("Y scale") ///
option("yscale")
CHECKBOX ck_reverse 10 200 120 ., ///
label("Reverse y-axis") ///
option("reverse")
CHECKBOX ck_nolines 10 220 120 ., ///
label("Suppress lines") ///
option("nolines")
CHECKBOX ck_eform 150 200 120 ., ///
label("Exponential form") ///
option("eform")
CHECKBOX ck_forcenull 150 220 120 ., ///
label("Force null") ///
option("forcenull")
CHECKBOX ck_msymbol 290 200 110 ., ///
label("Marker Symb:") ///
onclickon(main.ed_msymbol.enable) ///
onclickoff(main.ed_msymbol.disable)
EDIT ed_msymbol 290 220 110 ., ///
label("M Symbol") ///
option("msymbol")
END
INCLUDE ifin
SCRIPT main_se_on
BEGIN
main.tx_se.show
main.vl_se.show
main.tx_se.enable
main.vl_se.enable
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_se_off
BEGIN
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
END
SCRIPT main_var_on
BEGIN
main.tx_var.show
main.vl_var.show
main.tx_var.enable
main.vl_var.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_var_off
BEGIN
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_on
BEGIN
main.tx_ci.show
main.vl_ci.show
main.tx_ci.enable
main.vl_ci.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
SCRIPT main_ci_off
BEGIN
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
PROGRAM command
BEGIN
put "metafunnel "
if main.r_se {
varlist main.vl_se
}
if main.r_var {
varlist main.vl_var
}
if main.r_ci {
varlist main.vl_ci
}
INCLUDE _ifin_pr
beginoptions
option radio(main r_se r_var r_ci)
optionarg main.vn_by
optionarg main.ed_subtitle
optionarg main.ed_xtitle
optionarg main.ed_ytitle
optionarg main.ed_xscale
optionarg main.ed_yscale
option main.ck_nolines
option main.ck_forcenull
option main.ck_eform
option main.ck_reverse
optionarg main.ed_msymbol
endoptions
END

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{smcl}
{* 19aug2003}{...}
{hline}
help for {hi:metafunnel} {right:dialog: {dialog metafunnel}{space 20}}
{hline}
{title:Funnel plots for meta-analysis}
{p 8 15 2}
{cmd:metafunnel}
{it:{ theta { se | var } | exp(theta) ll ul [cl] }}
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{cmd:,}
{cmdab:ef:orm}
{cmdab:nol:ine}
{cmdab:rev:erse}
{cmdab:forc:enull}
{cmdab:graph_options}
]
{title:Description}
{p 4 4 2}
{cmd:metafunnel} plots funnel plots. These graphical displays are used to
examine whether the results of a meta-analysis may have been affected by
publication or other types of bias.
{title:Options}
{p 4 8 2}
{cmd:by(}{it:byvarname}{cmd:)} displays subgroups according to the value
of {it: byvarname}. The legend
displays the value labels for the levels of {it: byvarname} if these are
present; otherwise it displays the value of each level of{it: byvarname}.
{p 4 8 2}
{cmd:eform} exponentiates the treatment effect theta and displays the
horizontal axis (treatment effect) on a log scale. This is useful for
displaying ratio measures such as odds ratios and risk ratios.
{p 4 8 2}
{cmd:reverse} inverts the funnel plot so that larger studies are displayed
at the bottom of the plot with smaller studies at the top.
This may also be achieved by specifying {cmd:noreverse} as part of the
{cmd: yscale(}{it:axis_description}{cmd:)} graphics option.
{p 4 8 2}
{cmd:noline} specifies that pseudo 95% confidence interval lines
should not be included in the plot. The default is to include them.
{p 4 8 2}
{cmd:forcenull} forces the vertical line at the centre of the funnel
to be plotted at the null treatment effect of zero (1 when the treatment
effect is exponentiated). The default is for the line to be plotted at
the value of the fixed effect summary estimate.
{title:Remarks}
{p 4 4 2}
Funnel plots are simple graphical displays of a measure of study size
on the vertical axis against intervention or treatment effect on the
horizontal axis. The name "funnel plot" is based on the fact that the
precision in the estimation of the underlying intervention or treatment
effect will increase as the size of component studies increases. Results
from small studies will therefore scatter more widely, with the spread
narrowing among larger studies. In the absence of bias the plot will
resemble a symmetrical inverted funnel.
{p 4 4 2}
If there is bias, for example because smaller studies showing no
statistically significant effects remain unpublished, then such
publication bias will lead to an asymmetrical appearance of the funnel
plot. It should be noted that although funnel plots have traditionally
been used to examine evidence for publication bias, funnel plot asymmetry
may reflect other types of bias, or even result from the true intervention
or treatment effect differing between small and large studies. They should
thus be seen as displaying the evidence for "small study effects" in general
rather than publication bias in particular. These issues are discussed by
Egger {it: et al.} (1997) and Sterne {it:et al.} (2001a).
{p 4 4 2}
{cmd:metafunnel} uses the same syntax as other meta-analysis commands
such as {help meta}, {help metabias}, {help metainf} and {help metatrim}.
The user provides an estimate of the treatment or intervention effect,
{it: theta}, together with its associated standard error {it: se_theta}
(the default) or variance {it: var_theta}, (in which case the {cmd: var}
option should be specified). Alternatively, the user provides {it:exp(theta)}
(i.e., a risk ratio or odds ratio) and its confidence interval,
{it: (ll, ul)}).
{p 4 4 2}
The funnel plots are displayed in line with meta-analytic convention and
the recommendations of Sterne et al. (2001b). The effect of the
treatment or intervention in each study (horizontal axis) is plotted
against the study size, as measured by the standard error of the treatment
or intervention effect.
(vertical axis). The vertical axis is reversed so that larger studies are
displayed towards the top of the graph (this behaviour may be changed using
the {cmd: reverse} option). Users who wish to plot the treatment effect on the vertical axis should use the {cmd:graph(begg)} option of
the {help metabias} command. The {help funnel} command, which is part
of the {help metan} package, also provides an alternative way to draw
funnel plots.
{p 4 4 2}
The plots include pseudo-95% confidence interval lines, which are
drawn around the summary fixed-effect estimate of the intervention or
treatment effect. The lines may be omitted using the {cmd:nolines} option.
The user may also specify that the pseudo confidence limits are centred
around a zero intervention effect using the {cmd:forcenull} option.
{p 4 4 2}
When the {cmd:eform} option is used, the label of the horizontal axis
(treatment effect, {it:theta}) is
changed accordingly, unless there is a variable label for {cmd: theta} or
the {cmd: xtitle(}{it:axis_title}{cmd:)} graphics option is used.
{p 4 4 2}
By default, the subtitle "Funnel plot with pseudo 95% confidence limits"
is displayed. This may be changed using the graphics option
{cmd:subtitle(}{it:tinfo}{cmd:)}.
{title:Examples}
{p 4 8 2}{cmd:. metafunnel meandiff semeandiff}
{p 4 8 2}{cmd:. metafunnel logor selogor, eform xtitle("Odds ratio (log scale)")}
{p 4 8 2}{cmd:. metafunnel sttd stderr, by(dose) subtitle(Funnel plot with subgroups) forcenull}
{p 4 8 2}{cmd:. metafunnel logor varlogor, var reverse nolines xtitle(log odds ratio)}
{title:Acknowledgements}
{p 4 4 2}
{cmd:metafunnel} was written by Jonathan Sterne, University of Bristol.
Portions of the code were originally written by Tom Steichen, who also
gave helpful comments on an early version of the command and provided
the dialog.
{title:References}
{p 4 8 2}Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997).
Bias in meta-analysis detected by a simple, graphical test.
{it:British Medical Journal} 315: 629-634
{p 4 8 2}Sterne, J.A.C., Egger, M. and Davey Smith, G. (2001a).
Investigating and dealing with publication and other biases
in meta-analysis. {it: British Medical Journal} 323: 101-105
{p 4 8 2}Sterne, J.A.C. & Egger, M. (2001b). Funnel plots for detecting
bias in meta-analysis: guidelines on choice of axis.
{it: Journal of Clinical Epidemiology} 54: 1046-1055
{title:Also see}
{p 4 13 2}
Online: help for {help meta}, {help metabias},
{help metainf}, {help metatrim}, {help metan}, {help funnel} (if installed)
{p_end}

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*! metainf.ado AT version 3.0.0 March 2000 (STB-56: sbe26.1)
* metainf.ado AT version 2.0.0 November 1998
* metainf.ado AT version 1.0.0 February 1998
program define metainf
version 6.0
syntax varlist(min=2 max=2 numeric) [if] [in] [, id(varname) /*
*/ random eform t1(str) t2(str) Format(str) print `options' *]
tokenize `varlist'
local E `1'
local SE `2'
preserve
* Dealing with if and in options
if ("`if'"!="") {
qui keep `if'
}
if ("`in'"!="") {
qui keep `in'
}
* Overall estimates
qui meta `E' `SE', `eform'
if "`random'"=="random" {
local ove=$S_7
local ll=$S_9
* Global macro should be updated if meta change it name
local ul=$S_0
}
else {
local ove=$S_1
local ll=$S_3
local ul=$S_4
}
* Meta-analysis estimate ommiting one study each step
tempvar theta setheta ulth llth
qui sum `E', detail
local n=_result(1)
qui {
gen `theta'=.
gen `setheta'=.
gen `ulth'=.
gen `llth'=.
}
local i=1
tempvar s
qui gen `s'=_n
while (`i'<=`n') {
qui {
meta `E' `SE' if `s'!=`i', `eform'
if "`random'"=="random" {
replace `theta'=$S_7 in `i'
replace `llth'=$S_9 in `i'
replace `ulth'=$S_0 in `i'
}
else {
replace `theta'=$S_1 in `i'
replace `llth'=$S_3 in `i'
replace `ulth'=$S_4 in `i'
}
}
local i=`i'+1
}
* Maximum and minimum CI values
qui sum `llth', detail
local mnx=r(min)
qui sum `ulth', detail
local mxx=r(max)
* Labeling plot
if "`t2'" == "" {
local t2 "Study ommited"
}
if "`t1'" == "" {
if "`eform'"=="eform" & "`random'"=="" {
local t1 "Meta-analysis fixed-effects estimates (exponential form)"
}
if "`eform'"=="" & "`random'"=="" {
local t1 "Meta-analysis fixed-effects estimates (linear form)"
}
if "`eform'"=="eform" & "`random'"=="random" {
local t1 "Meta-analysis random-effects estimates (exponential form)"
}
if "`eform'"=="" & "`random'"=="random" {
local t1 "Meta-analysis random-effects estimates (linear form)"
}
}
* Numeric format
if "`format'" == "" {
local format "%5.2f"
}
* Print option
if "`print'"=="print" & "`eform'"=="" {
dis
dis in gre "`lab'"
dis in gr "------------------------------------------------------------------------------"
dis in gr _col(2) "Study ommited" _col(20) "|" _col(24) "Coef." _col(39) "[95% Conf. Interval]"
dis in gr "-------------------+----------------------------------------------------------"
local i=1
while `i'<=`n' {
if "`id'"=="" { local a=`s' in `i' }
else { local a=`id' in `i'}
local b=`theta' in `i'
local c=`llth' in `i'
local d=`ulth' in `i'
display _col(2) "`a'" _col(20) in gr "|" in ye _col(24) `b' _col(39) `c' _col(52) `d'
local i=`i'+1
}
dis in gr "-------------------+----------------------------------------------------------"
dis _col(2) "Combined" _col(20) in gr "|" in ye _col(24) `ove' _col(39) `ll' _col(52) `ul'
dis in gr "------------------------------------------------------------------------------"
}
else if "`print'"=="print" & "`eform'"=="eform" {
dis
dis in gre "`lab'"
dis in gr "------------------------------------------------------------------------------"
dis in gr _col(2) "Study ommited" _col(20) "|" _col(24) "e^coef." _col(39) "[95% Conf. Interval]"
dis in gr "-------------------+----------------------------------------------------------"
local i=1
while `i'<=`n' {
if "`id'"=="" {
local a=`s' in `i'
}
else {
local a=`id' in `i'
}
local b=`theta' in `i'
local c=`llth' in `i'
local d=`ulth' in `i'
display _col(2) "`a'" _col(20) in gr "|" in ye _col(24) `b' _col(39) `c' _col(52) `d'
local i=`i'+1
}
dis in gr "-------------------+----------------------------------------------------------"
display _col(2) "Combined" _col(20) in gr "|" in ye _col(24) `ove' _col(39) `ll' _col(52) `ul'
dis in gr "------------------------------------------------------------------------------"
}
* Displaying plot
hplot `theta' `llth' `ulth', r sy(o||) l("`id'") t1(`t1') t2(`t2') f(`format') /*
*/ xline(`ove',`ll',`ul') xlab(`mnx',`ove',`ll',`ul',`mxx') xti(`ove',`ll',`ul') /*
*/ xscale(`mnx',`mxx')
end

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/*
*! metainf dialog version 1.0.0, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metainf version 3.0.0, Mar 2000, Aurelio Tobias, atobias@isciii.es
Influence of a single study in meta-analysis estimation
-------------------------------------------------------
Syntax: metainf theta setheta [if exp] [in range]
[, id(labvar) eform random print graph_options]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-based Influence Analysis (metain&f)" "db metainf"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metainf")
RESET res1, label("Reset")
DIALOG main, label("metainf 3.0.0 - Meta-based Influence Analysis") tabtitle("Main")
BEGIN
TEXT tx_effect 10 5 330 ., ///
label("Vars for Effect Sizes: theta, se(theta), in that order")
VARLIST vl_effect @ _ss @ ., ///
label("Effect Sizes: theta, se(theta)")
CHECKBOX cb_id 10 60 40 ., ///
label("ID:") ///
onclickon(main.vn_id.enable) ///
onclickoff(main.vn_id.disable)
VARNAME vn_id 50 60 290 ., ///
label("ID Variable") ///
option("id")
GROUPBOX gb_pool 10 85 195 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 15 100 185 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ 125 @ ., ///
label("Random, Inverse Variance") last ///
option("random")
CHECKBOX cb_eform 230 95 200 ., ///
label("Use exp(theta)") ///
option("eform")
CHECKBOX cb_print 230 125 200 ., ///
label("Print Details") ///
option("print")
GROUPBOX gb_hpopts 10 155 330 _ht1h, ///
label("Allowed hplot Options:")
EDIT ed_hpopts 15 175 320 ., ///
label("hplot Options")
END
INCLUDE ifin
PROGRAM command
BEGIN
put "metainf "
varlist main.vl_effect
INCLUDE _ifin_pr
beginoptions
option radio(main r_fixed r_random)
optionarg main.vn_id
option main.cb_eform
option main.cb_print
put main.ed_hpopts
endoptions
END

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.-
help for ^metainf^ (STB-47: sbe26; STB-56: sbe26.1)
.-
Influence of a single study in the meta-analysis estimates
----------------------------------------------------------
^metainf^ theta setheta [^if^ exp] [^in^ range] [,^id(^labvar^)^ ^eform^ ^random^
^print^ graph_options]
Description
-----------
^metainf^ investigate the influence of a single study on the overall meta-
analysis estimate. This command shows graphically the results of an influence
analysis, in which the meta-analysis estimates are computed omitting one study
in each turn.
^theta^ is the effect estimated from the individual study, and ^setheta^ is its
standard error. For example theta might be a difference in means, a log rate
ratio, a log odds ratio, or a log hazard ratio.
If you have a dataset which contains data for all studies, then the @byvar@
command can be used to derive the effect estimates and standard errors for the
individual studies. For example:
. ^sort study^
. ^byvar study, coef(group) se(group) generate:^
. ^quietly poisson cases group, e(pyrs)^
. ^sort study^
. ^qui by study: keep if _n==1^
. ^rename _C_1 logrr^
. ^rename _S_1 logse^
. ^metainf logrr logse, id(study)^
Alternatively, the @collapse@ command may be useful.
Options
-------
^id(^labvar^)^ is a variable which is used to label the studies.
^eform^ requests that the output is exponentiated.
^random^ request random-effects estimates. By default fixed-effects estimates
are computed.
^print^ shows the estimates, and their 95% confidence interval, of the meta-
analysis estimates omitting one study in each turn. These values are used to
make the graphic.
All ^hplot^ options are also available.
Note
----
To run the ^metainf^ command, the ^meta^ [STB-43 sbe16.2, STB-42 sbe16.1,
STB-38 sbe16] and the ^hplot^ (Cox 1999a, 1999b) command must be installed.
Examples
--------
. ^metainf logrr logse, print id(studyid)^
. ^metainf logrr logse, random print id(studyid)^
. ^metainf logrr logse, random eform id(studyid) format(%4.2f)^
References
----------
Cox NJ (1999a). -hplot- and -hbar- for presentation graphics. Proceedings
of the 5th Stata UK user group meeting, London, UK.
Cox NJ (1999b). De tablas a gr<67>ficos, presentaciones en Stata. Proceedings
of the 1st Iberian user group meeting, Cordoba, Spain.
Author
------
Aurelio Tobias
Universidad Miguel Hernandez, Alicante, Spain
email: bledatobias@@ctv.es
Also see
--------
On-line: help for @byvar@, @collapse@, @for@, @meta@ (if installed), @metareg@ (if
installed), @metabias@ (if installed), @metacum@ (if installed), @galbr@
(if installed), metap (if installed), @hplot@ (if installed)

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/*
*! metan dialog version 1.0.5, 1 Apr 2004, T. J. Steichen, steichen@triad.rr.com
*! for metan version 1.85, 15 Mar 2004, M. J. Bradburn, mike.bradburn@cancer.org.uk
Fixed and random effects meta-analysis
--------------------------------------
Syntax: metan varlist [if exp] [in range] [,
label(namevar=name, yearvar=year)
fixed random fixedi randomi peto
rr or rd cornfield chi2 breslow notable nograph
cohen hedges glass nostandard
log sortby(sort_vars) ilevel(#) olevel(#) nokeep
xlabel(#,..,#) force t1title(#,..,#) boxsha(#) boxsca(#) texts(#)
saving(filename) nowt counts nostats nooverall
group1(text) group2(text) effect(text) nobox eform
wgt(weightvar) xtick(#,..,#)
t2title(#,..,#) b1title(#,..,#) b2title(#,..,#)
nointeger cc(#) by(byvar) nosubgroup sgweight legend(text) ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-analysis of Binary and Continuous Data (meta&n)" "db metan"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_medium
INCLUDE _ht300
DEFINE _dlgwd 450
INCLUDE header
HELP hlp1, view("help metan")
RESET res1, label("Reset")
DIALOG main, label("metan 1.85 - Meta-analysis of Binary & Continuous") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 310 _ht1h, ///
label("Type of Data:")
RADIO r_binary 15 25 60 ., ///
label(" Count") first ///
onclickon(script main_binary_on) ///
onclickoff(script main_binary_off)
RADIO r_continuous 75 25 90 ., ///
label("Continuous") middle ///
onclickon(script main_continuous_on) ///
onclickoff(script main_continuous_off)
RADIO r_effect_ci 165 25 75 ., ///
label("Effect/CI") middle ///
onclickon(script main_effect_ci_on) ///
onclickoff(script main_effect_ci.off)
RADIO r_effect_se 240 25 75 ., ///
label("Effect/SE") last ///
onclickon(script main_effect_se_on) ///
onclickoff(script main_effect_se_off)
/* end GROUPBOX gb_data */
TEXT tx_binary 10 50 310 ., ///
label("Vars for Counts: a, b, c, d, in that order")
VARLIST vl_binary @ _ss @ ., ///
label("Vars for a, b, c, d")
TEXT tx_contin_exp @ 50 310 ., ///
label("Vars for Exp. Group: n, mean, sd, in that order")
VARLIST vl_contin_exp @ _ss @ ., ///
label("Experimental Group: n, mean, sd")
TEXT tx_contin_ctl @ _ss 310 ., ///
label("Vars for Control Group: n, mean, sd, in that order")
VARLIST vl_contin_ctl @ _ss @ ., ///
label("Control Group: n, mean, sd")
TEXT tx_effect_ci @ 50 310 ., ///
label("Vars for Effects: theta, lowerCI, upperCI, in that order")
VARLIST vl_effect_ci @ _ss @ ., ///
label("Effect Sizes: theta, lowerCI, upperCI")
TEXT tx_effect_se @ 50 310 ., ///
label("Vars for Effects: theta, se(theta), in that order")
VARLIST vl_effect_se @ _ss @ ., ///
label("Effect Sizes: theta, se(theta)")
/* end vars */
GROUPBOX gb_labels 10 130 310 _ht3h, ///
label("Labels for Data:")
CHECKBOX cb_name 20 150 70 ., ///
label("Name:") ///
onclickon(main.vn_name.enable) ///
onclickoff(main.vn_name.disable)
VARNAME vn_name 100 150 210 ., ///
label("Name Variable")
CHECKBOX cb_year 20 170 70 ., ///
label("Year:") ///
onclickon(main.vn_year.enable) ///
onclickoff(main.vn_year.disable)
VARNAME vn_year 100 170 210 ., ///
label("Year Variable")
/* end GROUPBOX gb_labels */
GROUPBOX gb_by 10 200 310 45, ///
label("By Variable:")
CHECKBOX cb_by 20 220 70 ., ///
label(" By:") ///
onclickon(script main_by_on) ///
onclickoff(script main_by_off)
VARLIST vl_by 100 220 210 ., ///
label("By Variable") ///
option("by")
/* end GROUPBOX gb_by */
GROUPBOX gb_sortby 10 250 310 45, ///
label("Sort Data:")
CHECKBOX cb_sortby 20 270 70 ., ///
label(" By:") ///
onclickon(main.vl_sortby.enable) ///
onclickoff(main.vl_sortby.disable)
VARLIST vl_sortby 100 270 210 ., ///
label("Name Variable") ///
option("sortby")
/* end GROUPBOX gb_sortby */
GROUPBOX gb_opts 325 5 120 85, ///
label("General Options:")
CHECKBOX cb_nokeep 330 25 70 ., ///
label("noKeep") ///
option("nokeep")
CHECKBOX cb_ilevel 330 45 60 ., ///
label("ilevel:") ///
onclickon(main.ed_ilevel.enable) ///
onclickoff(main.ed_ilevel.disable)
EDIT ed_ilevel 400 45 40 ., ///
label("ilevel") ///
numonly default(global S_level) ///
option("ilevel")
CHECKBOX cb_olevel 330 65 60 ., ///
label("olevel:") ///
onclickon(main.ed_olevel.enable) ///
onclickoff(main.ed_olevel.disable)
EDIT ed_olevel 400 65 40 ., ///
label("olevel") ///
numonly default(global S_level) ///
option("olevel")
/* end GROUPBOX gb_opts */
FRAME fr_wgt 325 95 120 60
CHECKBOX cb_wgt 330 100 110 ., ///
label("Weight Var") ///
onclickon(main.vn_wgt.enable) ///
onclickoff(main.vn_wgt.disable)
VARNAME vn_wgt 330 121 110 ., ///
label("Weight Variable") ///
option("wgt")
/* end wgt */
GROUPBOX gb_by_opts 325 200 120 61, ///
label("By Options:")
CHECKBOX cb_nosubgroup 330 220 100 ., ///
label("noSubGroup") ///
option("nosubgroup")
CHECKBOX cb_sgweight 330 240 100 ., ///
label("sgWeight") ///
option("sgweight")
/* end GROUPBOX gb_by_opts */
END
DIALOG metan_b, tabtitle("Binary...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht9h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Mantel-Haenszel") first ///
onclickoff(program metan_b_bd) ///
option("fixed")
RADIO r_fixedi @ _ss @ ., ///
label("Fixed, Inverse Variance") middle ///
onclickoff(program metan_b_bd) ///
option("fixedi")
RADIO r_random @ _ss 195 ., ///
label("Random (M-H heterogeneity)") middle ///
onclickon(script metan_b_random_on) ///
onclickoff(program metan_b_chi2) ///
option("random")
RADIO r_randomi @ _ss @ ., ///
label("Random (I-V heterogeneity)") middle ///
onclickon(script metan_b_random_on) ///
onclickoff(program metan_b_chi2) ///
option("randomi")
RADIO r_peto @ _ss @ ., ///
label("Peto") last ///
onclickon(script metan_b_peto_on) ///
onclickoff(program metan_b_cornfield) ///
option("peto")
GROUPBOX gb_stat 220 10 120 _ht5h, ///
label("Statistic")
RADIO r_rr 230 30 80 ., ///
label("RR") first ///
onclickon(script metan_b_rr_on) ///
onclickoff(program metan_b_rr_off) ///
option("rr")
RADIO r_or @ _ss @ ., ///
label("OR") middle ///
onclickon(program metan_b_or_on) ///
onclickoff(program metan_b_or_off) ///
option("or")
RADIO r_rd @ _ss @ ., ///
label("RD") last ///
option("rd")
CHECKBOX cb_cornfield 10 140 200 ., ///
label("Use Cornfield CI's") ///
option("cornfield")
CHECKBOX cb_chi2 10 160 200 ., ///
label("Use Chi-2 Statistic") ///
option("chi2")
CHECKBOX cb_breslow 10 180 200 ., ///
label("Use Breslow-Day test") ///
option("breslow")
CHECKBOX cb_log 230 100 200 ., ///
label("Log scaled") ///
option("log")
CHECKBOX cb_eform 230 120 200 ., ///
label("Exponentiate") ///
option("eform")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_cc 10 260 150 ., ///
label("Continuity Correction:") ///
onclickon(metan_b.ed_cc.enable) ///
onclickoff(metan_b.ed_cc.disable)
EDIT ed_cc 160 260 65 ., ///
label("Continuity Correction") ///
option("cc")
CHECKBOX cb_nointeger 230 260 200 ., ///
label("noInteger") ///
option("nointeger")
END
DIALOG metan_c, tabtitle("Continuous...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random (I-V heterogeneity)") last ///
option("random")
GROUPBOX gb_stat 220 10 120 _ht7h, ///
label("Statistic")
RADIO r_cohen 230 30 100 ., ///
label("Cohen") first ///
option("cohen")
RADIO r_hedges @ _ss @ ., ///
label("Hedges") middle ///
option("hedges")
RADIO r_glass @ _ss @ ., ///
label("Glass") middle ///
option("glass")
RADIO r_nostandard @ _ss @ ., ///
label("noStandard") last ///
option("nostandard")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_nointeger 230 260 200 ., ///
label("noInteger") ///
option("nointeger")
END
DIALOG metan_e, tabtitle("Effect...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random (I-V heterogeneity)") last ///
option("random")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_effect 10 110 100 ., ///
label("Effect Label:") ///
onclickon(metan_e.ed_effect.enable) ///
onclickoff(metan_e.ed_effect.disable)
EDIT ed_effect 115 @ 225 ., ///
label("effect label") ///
option("effect")
END
DIALOG graph, tabtitle("Graph Opts")
BEGIN
CHECKBOX cb_saving 10 10 100 ., ///
label("Save graph:") ///
onclickon(graph.fi_saving.enable) ///
onclickoff(graph.fi_saving.disable)
FILE fi_saving 110 10 240 ., ///
label("File Name") ///
error("Save filename") ///
dialogtitle("Save graph") ///
filter("Stata Graphs|*.gph") ///
save ///
option("saving")
/* end saving */
GROUPBOX gb_xlabel 10 40 330 _ht3h, ///
label("X-axis Tick Labels (comma between):")
CHECKBOX cb_xlabel 15 60 45 ., ///
label("Set:") ///
onclickon(script graph_xlabel_on) ///
onclickoff(script graph_xlabel_off)
EDIT ed_xlabel 65 @ 270 ., ///
label("X Tick labels") ///
option("xlabel")
CHECKBOX cb_force 15 80 320 ., ///
label("Force Scale to Tick Range") ///
option("force")
/* end tick labels */
CHECKBOX cb_counts 15 115 65 ., ///
label("Counts") ///
onclickon(script graph_counts_on) ///
onclickoff(script graph_counts_off) ///
option("counts")
CHECKBOX cb_nowt 15 145 85 ., ///
label("noWeights") ///
option("nowt")
CHECKBOX cb_nostats 15 165 85 ., ///
label("noStats") ///
option("nostats")
CHECKBOX cb_nooverall 115 145 85 ., ///
label("noOverall") ///
option("nooverall")
CHECKBOX cb_nobox 115 165 85 ., ///
label("noBox") ///
option("nobox")
/* end switches */
CHECKBOX cb_legend 115 185 70 ., ///
label("Legend:") ///
onclickon(graph.ed_legend.enable) ///
onclickoff(graph.ed_legend.disable)
EDIT ed_legend 190 @ 55 ., ///
label("legend") ///
option("legend")
CHECKBOX cb_grp1 85 115 55 ., ///
label("Grp 1:") ///
onclickon(graph.ed_grp1.enable) ///
onclickoff(graph.ed_grp1.disable)
EDIT ed_grp1 145 @ 90 ., ///
label("Grp1") ///
option("group1")
CHECKBOX cb_grp2 245 115 55 ., ///
label("Grp 2:") ///
onclickon(graph.ed_grp2.enable) ///
onclickoff(graph.ed_grp2.disable)
EDIT ed_grp2 305 @ 90 ., ///
label("Grp2") ///
option("group2")
CHECKBOX cb_boxy 255 145 95 ., ///
label("Box yscale:") ///
onclickon(graph.ed_boxy.enable) ///
onclickoff(graph.ed_boxy.disable)
EDIT ed_boxy 355 @ 40 ., ///
label("Box yscale") numonly default(1) ///
option("boxsca")
CHECKBOX cb_boxs 255 165 95 ., ///
label("Box shade:") ///
onclickon(graph.ed_boxs.enable) ///
onclickoff(graph.ed_boxs.disable)
EDIT ed_boxs 355 @ 40 ., ///
label("Box shading") numonly default(4) ///
option("boxsha")
CHECKBOX cb_texts 255 185 95 ., ///
label("Font scale:") ///
onclickon(graph.ed_texts.enable) ///
onclickoff(graph.ed_texts.disable)
EDIT ed_texts 355 @ 40 ., ///
label("Font scale") numonly default(1) ///
option("texts")
/* end sets */
GROUPBOX gb_gopts7 10 205 390 _ht1h, ///
label("Allowed Graph7 Options:")
EDIT ed_gopts7 15 225 380 ., ///
label("Graph7 Options")
CHECKBOX cb_b1 15 255 390 ., ///
label("Effect Direction Labels, option b1( ):") ///
onclickon(script graph_b1_on) ///
onclickoff(script graph_b1_off)
TEXT tx_b1_l 10 275 25 ., ///
label("Left:")
EDIT ed_b1_l 35 275 160 ., ///
label(" Left")
TEXT tx_b1_r 205 275 35 ., ///
label("Right:")
EDIT ed_b1_r 240 275 160 ., ///
label("Right")
/* end Graph7 opts */
END
INCLUDE ifin
SCRIPT main_binary_on
BEGIN
main.tx_binary.show
main.vl_binary.show
main.tx_binary.enable
main.vl_binary.enable
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
graph.cb_counts.enable
END
SCRIPT main_binary_off
BEGIN
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
graph.cb_counts.disable
END
SCRIPT main_continuous_on
BEGIN
main.tx_contin_exp.show
main.vl_contin_exp.show
main.tx_contin_exp.enable
main.vl_contin_exp.enable
main.tx_contin_ctl.show
main.vl_contin_ctl.show
main.tx_contin_ctl.enable
main.vl_contin_ctl.enable
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
END
SCRIPT main_continuous_off
BEGIN
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_ci_on
BEGIN
main.tx_effect_ci.show
main.vl_effect_ci.show
main.tx_effect_ci.enable
main.vl_effect_ci.enable
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_ci_off
BEGIN
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
END
SCRIPT main_effect_se_on
BEGIN
main.tx_effect_se.show
main.vl_effect_se.show
main.tx_effect_se.enable
main.vl_effect_se.enable
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_se_off
BEGIN
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
END
SCRIPT main_by_on
BEGIN
main.vl_by.enable
main.gb_by_opts.enable
main.cb_nosubgroup.enable
main.cb_sgweight.enable
END
SCRIPT main_by_off
BEGIN
main.vl_by.disable
main.gb_by_opts.disable
main.cb_nosubgroup.disable
main.cb_sgweight.disable
END
SCRIPT metan_b_random_on
BEGIN
metan_b.cb_breslow.disable
metan_b.cb_chi2.disable
END
SCRIPT metan_b_peto_on
BEGIN
metan_b.cb_breslow.disable
metan_b.cb_cornfield.disable
END
PROGRAM metan_b_bd
BEGIN
if metan_b.r_or {
call metan_b.cb_breslow.enable
}
END
PROGRAM metan_b_chi2
BEGIN
if metan_b.r_or & !(metan_b.r_random | metan_b.r_randomi) {
call metan_b.cb_chi2.enable
}
END
PROGRAM metan_b_cornfield
BEGIN
if metan_b.r_or {
call metan_b.cb_cornfield.enable
}
END
SCRIPT metan_b_rr_on
BEGIN
metan_b.cb_log.enable
metan_b.cb_eform.enable
END
PROGRAM metan_b_or_on
BEGIN
call metan_b.cb_log.enable
call metan_b.cb_eform.enable
call metan_b.r_peto.enable
if !metan_b.r_peto {
call metan_b.cb_cornfield.enable
}
if !(metan_b.r_random | metan_b.r_randomi) {
call metan_b.cb_chi2.enable
}
call metan_b.cb_breslow.enable
END
PROGRAM metan_b_or_off
BEGIN
if !metan_b.r_rr {
call metan_b.cb_log.disable
call metan_b.cb_eform.disable
}
call metan_b.r_peto.disable
call metan_b.cb_cornfield.disable
call metan_b.cb_chi2.disable
call metan_b.cb_breslow.disable
END
PROGRAM metan_b_rr_off
BEGIN
if !metan_b.r_or {
call metan_b.cb_log.disable
call metan_b.cb_eform.disable
}
END
SCRIPT graph_xlabel_on
BEGIN
graph.ed_xlabel.enable
graph.cb_force.enable
END
SCRIPT graph_xlabel_off
BEGIN
graph.ed_xlabel.disable
graph.cb_force.disable
END
SCRIPT graph_counts_on
BEGIN
graph.cb_grp1.enable
graph.cb_grp2.enable
END
SCRIPT graph_counts_off
BEGIN
graph.cb_grp1.disable
graph.cb_grp2.disable
graph.cb_grp1.setoff
graph.cb_grp2.setoff
graph.ed_grp1.disable
graph.ed_grp2.disable
END
SCRIPT graph_b1_on
BEGIN
graph.tx_b1_l.enable
graph.ed_b1_l.enable
graph.tx_b1_r.enable
graph.ed_b1_r.enable
END
SCRIPT graph_b1_off
BEGIN
graph.tx_b1_l.disable
graph.ed_b1_l.disable
graph.tx_b1_r.disable
graph.ed_b1_r.disable
END
PROGRAM command
BEGIN
put " metan "
if main.r_binary {
varlist main.vl_binary
}
if main.r_continuous {
varlist main.vl_contin_exp main.vl_contin_ctl
}
if main.r_effect_ci {
varlist main.vl_effect_ci
}
if main.r_effect_se {
varlist main.vl_effect_se
}
INCLUDE _ifin_pr
beginoptions
if main.cb_name | main.cb_year {
put "label("
if main.cb_name {
put "namevar=" main.vn_name
}
if main.cb_name & main.cb_year {
put ", "
}
if main.cb_year {
put "yearvar=" main.vn_year
}
put ") "
}
optionarg main.vl_sortby
optionarg main.vl_by
optionarg main.ed_ilevel
optionarg main.ed_olevel
optionarg main.vn_wgt
option main.cb_nokeep
option main.cb_nosubgroup
option main.cb_sgweight
if main.r_binary {
if !main.vn_wgt {
option radio(metan_b r_fixed r_fixedi r_random r_randomi r_peto)
}
option radio(metan_b r_rr r_or r_rd)
option metan_b.cb_cornfield
option metan_b.cb_chi2
option metan_b.cb_breslow
option metan_b.cb_log
option metan_b.cb_eform
option metan_b.cb_nograph
option metan_b.cb_notable
optionarg metan_b.ed_cc
option metan_b.cb_nointeger
}
if main.r_continuous {
if !main.vn_wgt {
option radio(metan_c r_fixed r_random)
}
option radio(metan_c r_cohen r_hedges r_glass r_nostandard)
option metan_c.cb_nograph
option metan_c.cb_notable
option metan_c.cb_nointeger
}
if main.r_effect_se | main.r_effect_ci {
if !main.vn_wgt {
option radio(metan_e r_fixed r_random)
}
optionarg metan_e.ed_effect
option metan_e.cb_nograph
option metan_e.cb_notable
}
optionarg graph.fi_saving
optionarg graph.ed_xlabel
option graph.cb_force
option graph.cb_counts
option graph.cb_nowt
option graph.cb_nostats
option graph.cb_nooverall
option graph.cb_nobox
optionarg graph.ed_grp1
optionarg graph.ed_grp2
optionarg graph.ed_texts
optionarg graph.ed_legend
optionarg graph.ed_boxy
optionarg graph.ed_boxs
put graph.ed_gopts7 " "
if graph.cb_b1 {
put "b1(*I:" graph.ed_b1_l "*" graph.ed_b1_r ") "
}
endoptions
END

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@ -0,0 +1,919 @@
{smcl}
{* 7Jul2008}{...}
{hline}
help for {hi:metan}, {hi:labbe}
{hline}
{title:Fixed and random effects meta-analysis}
{p 8 12 2}
{cmd:metan}
{it:varlist}
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{it:weight}]
[{cmd:,}
{it:measure_and_model_options}
{it:options_for_continuous_data}
{it:output_options}
{it:forest_plot_options}
]
{p 12 12 2}
where {it:measure_and_model_options} may be
{p 12 12 2}
{cmd:or}
{cmd:rr}
{cmd:rd}
{cmd:fixed}
{cmd:random}
{cmd:fixedi}
{cmd:peto}
{cmd:cornfield}
{cmd:chi2}
{cmd:breslow}
{cmdab:noint:eger}
{cmd:cc(}{it:#}{cmd:)}
{cmd:wgt(}{it:weightvar}{cmd:)}
{cmd:second(}{it:model} or {it:estimates and description}{cmd:)}
{cmd:first(}{it:estimates and description}{cmd:)}
{p 12 12 2}
and where {it:options_for_continuous_data} may be
{p 12 12 2}
{cmd:cohen}
{cmd:hedges}
{cmd:glass}
{cmd:nostandard}
{cmd:fixed}
{cmd:random}
{p 12 12 2}
and where {it:output_options} may be
{p 12 12 2}
{cmd:by(}{it:byvar}{cmd:)}
{cmd:nosubgroup}
{cmd:sgweight}
{cmd:log}
{cmd:eform}
{cmd:efficacy}
{cmdab:il:evel(}{it:#}{cmd:)}
{cmdab:ol:evel(}{it:#}{cmd:)}
{cmd:sortby(}{it:varlist}{cmd:)}
{cmd:label(}{it:namevar yearvar}{cmd:)}
{cmd:nokeep}
{cmd:notable}
{cmd:nograph}
{cmd:nosecsub}
{p 12 12 2}
and where {it:forest_plot_options} may be
{p 12 12 2}
{cmd:legend(}{it:string}{cmd:)}
{cmdab:xla:bel(}{it:#},...{cmd:)}
{cmdab:xt:ick(}{it:#},...{cmd:)}
{cmd:boxsca(}{it:#}{cmd:)}
{cmd:nobox}
{cmd:nooverall}
{cmd:nowt}
{cmd:nostats}
{cmd:group1(}{it:string}{cmd:)}
{cmd:group2(}{it:string}{cmd:)}
{cmd:effect(}{it:string}{cmd:)}
{cmd:force}
{p 12 12 2}
...with further {it:forest_plot_options} in the version 9 update
{p 12 12 2}
{cmd:lcols(}{it:varlist}{cmd:)}
{cmd:rcols(}{it:varlist}{cmd:)}
{cmd:astext(}{it:#}{cmd:)}
{cmd:double}
{cmd:nohet}
{cmd:summaryonly}
{cmd:rfdist}
{cmdab:rfl:evel(}{it:#}{cmd:)}
{cmd:null(}{it:#}{cmd:)}
{cmd:nulloff}
{cmd:favours(}{it:string} # {it:string}{cmd:)}
{cmd:firststats(}{it:string}{cmd:)}
{cmd:secondstats(}{it:string}{cmd:)}
{cmd:boxopt(}{it:}{cmd:)}
{cmd:diamopt(}{it:}{cmd:)}
{cmd:pointopt(}{it:}{cmd:)}
{cmd:ciopt(}{it:}{cmd:)}
{cmd:olineopt(}{it:}{cmd:)}
{cmd:classic}
{cmd:nowarning}
{cmd:dp(}{it:#}{cmd:)}
{it:graph_options}
{p 8 12 2}
{cmd:labbe}
{it:varlist}
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{it:weight}]
[{cmd:,}
{cmd:nowt}
{cmdab:per:cent}
{cmd:or(}{it:#}{cmd:)}
{cmd:rr(}{it:#}{cmd:)}
{cmd:rd(}{it:#}{cmd:)}
{cmd:null}
{cmd:logit}
{cmd:wgt(}{it:weightvar}{cmd:)}
{cmd:symbol(}{it:symbolstyle}{cmd:)}
{cmd:nolegend}
{cmd:id(}{it:idvar}{cmd:)}
{cmd:textsize(}{it:#}{cmd:)}
{cmd:clockvar(}{it:clockvar}{cmd:)}
{cmd:gap(}{it:#}{cmd:)}
{it:graph_options}
{title:Description}
{p 4 4 2}
These routines provide facilities to conduct meta-analyses of data from
more than one study and to graph the results. Either binary (event) or
continuous data from two groups may be combined using the {cmd:metan}
command. Additionally, intervention effect estimates with corresponding
standard errors or confidence intervals may be meta-analysed. Several
meta-analytic methods are available, and the results may be displayed
graphically in a Forest plot. A test of whether
the summary effect measure is equal to the null is given,
as well as a test for heterogeneity, i.e., whether the true effect in all
studies is the same. Heterogeneity is also quantified using the I-squared
measure (Higgins et al 2003).
{p 4 4 2}
{cmd:metan} (the main meta-analysis routine) requires either two, three, four
or six variables to be declared. When four variables are specified
these correspond to the number of events and non-events in the experimental group
followed by those of the control group, and
analysis of binary data is performed on the 2x2 table.
With six variables, the data are assumed
continuous and to be the sample size, mean and standard deviation of
the experimental group followed by those of the control group.
If three variables are specified these are assumed to be the effect estimate and
its lower and upper confidence interval, and it is suggested that these are
log transformed for odds ratios or risk ratios and the {cmd: eform} option used.
If two variables are specified these are assumed to be the effect estimate and standard
error; again, it is recommended that odds ratios or risk ratios are log transformed.
{p 4 4 2}
{cmd:labbe} draws a L'Abbe plot for event data (proportion of successes in the
two groups). This is an alternative to the graph produced by {cmd:metan}.
{p 4 4 2}
{cmd:funnel} may be used for producing a "funnel plot", a graph of either the
study sample size, standard error or precision (inverse of s.e.) against
the effect size.
{p 4 4 2}
Note that the {cmd:metan} command now requires
Stata version 9 and has been updated
with several new options. Changes are mainly to graphics options which
are collected in the section {it: Further options in the v9 update for metan: Forest plot},
or otherwise marked {it:v9 update}. The previous version is still available under
the name -metan7-
{title:Remarks on funnel (discontinued)}
{p 4 4 2}
The {cmd:metafunnel} command has more options for
funnel plots and version 8 graphics; as such {cmd:funnel} has been removed.
See {help metafunnel} (if installed)
{title:Options for metan}
{dlgtab:Specifying the measure and model}
{p 4 4 2}
These options apply to binary data.
{p 4 8 2}
{cmd:rr} pools risk ratios (the default).
{p 4 8 2}
{cmd:or} pools odds ratios.
{p 4 8 2}
{cmd:rd} pools risk differences.
{p 4 8 2}
{cmd:fixed} specifies a fixed effect model using the method of
Mantel and Haenszel (the default).
{p 4 8 2}
{cmd:fixedi} specifies a fixed effect model using the inverse variance method.
{p 4 8 2}
{cmd:peto} specifies that Peto's method is used to pool odds ratios.
{p 4 8 2}
{cmd:random} specifies a random effects model using the method of
DerSimonian & Laird.
{p 4 8 2}
{cmd:cornfield} computes confidence intervals for odds ratios by method of
Cornfield, rather than the (default) Woolf method.
{p 4 8 2}
{cmd:chi2} displays chi-squared statistic (instead of z) for the test
of significance of the pooled effect size. This is available only for
odds ratios pooled using Peto or Mantel-Haenszel methods.
{p 4 8 2}
{cmd:breslow} produces Breslow-Day test for homogeneity of ORs.
{p 4 8 2}
{cmd:cc(}{it:#}{cmd:)} defines a fixed continuity correction to add in the case where
a study contains a zero cell. By default, {cmd:metan}
adds 0.5 to each cell of a trial where a zero is encountered when
using Inverse-Variance, Der-Simonian & Laird or Mantel-Haenszel
weighting to enable finite variance estimators to be derived.
However, the {cmd:cc()} option allows the use of other constants
(including none). See also the {cmd:nointeger} option.
{p 4 8 2}
{cmd:nointeger} allows the cell counts to be non-integers. This may be useful
when a variable continuity correction is sought for studies containing
zero cells, but also may be used in other circumstances, such as where a
cluster-randomised trial is to be incorporated and the "effective sample
size" is less than the total number of observations.
{p 4 8 2}
{cmd:wgt(}{it:weightvar}{cmd:)} specifies alternative weighting
for any data type. The effect size is to be computed by assigning
a weight of {it:weightvar} to the studies. When RRs or ORs are declared,
their logarithms are weighted. You should only use this option if you are
satisfied that the weights are meaningful.
{p 4 8 2}
{cmd:second(}{it:model} or {it:estimates and description}{cmd:)} ({it:v9 update})
A second analysis may be performed using another method, using {cmd:fixed},
{cmd:random} or {cmd:peto}. Alternatively, the user may define their own
estimate and 95% CI based on calculations performed externally to {cmd:metan},
along with a description of their method, in the format
{it: es lci uci description}. The results of this analysis are then displayed
in the table and forest plot. Note that if {cmd:by} is used then sub-estimates
from the second method are not displayed with user defined estimates, for
obvious reasons.
{p 4 8 2}
{cmd:first(}{it:estimates and description}{cmd:)} ({it:v9 update})
Use of this command completely changes the way {cmd:metan} operates, as results
are no longer based on any standard methods. The user defines their own
estimate, 95% CI and description as in the above, and must supply their own
weightings using {cmd:wgt(}{it:weightvar}{cmd:)} to control display of box sizes. Note
that data must be supplied in the 2 or 3 variable syntax
({it:theta se_theta} or {it:es lci uci}) and
{cmd:by} may not be used used for obvious reasons.
{dlgtab:Continuous data}
{p 4 8 2}
{cmd:cohen} pools standardised mean differences by the method of Cohen
(the default).
{p 4 8 2}
{cmd:hedges} pools standardised mean differences by the method of Hedges.
{p 4 8 2}
{cmd:glass} pools standardised mean differences by the method of Glass.
{p 4 8 2}
{cmd:nostandard} pools unstandardised mean differences.
{p 4 8 2}
{cmd:fixed} specifies a fixed effect model using the inverse variance method
(the default).
{p 4 8 2}
{cmd:random} specifies a random effects model using the DerSimonian & Laird
method.
{p 4 8 2}
{cmd:nointeger} denotes that the number of observations in each arm does not
need to be an integer. By default, the first and fourth variables specified
(containing N_intervention and N_control respectively) may occasionally be
non-integer (see entry for {cmd:nointeger} under binary data).
{dlgtab:Output}
{p 4 8 2}
{cmd:by()} specifies that the meta-analysis is to be stratified
according to the variable declared.
{p 4 8 2}
{cmd:sgweight} specifies that the display is to present the percentage
weights within each subgroup separately. By default {cmd:metan} presents
weights as a percentage of the overall total.
{p 4 8 2}
{cmd:log} reports the results on the log scale
(valid for OR and RR analyses from raw data counts only).
{p 4 8 2}
{cmd:nosubgroup} indicates that no within-group results are to be
presented. By default {cmd:metan} pools trials both within and across
all studies.
{p 4 8 2}
{cmd:eform} exponentiates all effect sizes and confidence intervals
(valid only when the input variables are log odds ratios or log
hazard ratios with standard error or confidence intervals).
{p 4 8 2}
{cmd:efficacy} expresses results as the vaccine efficacy (the proportion
of cases that would have been prevented in the placebo group that
would have been prevented had they received the vaccination).
Only available with odds ratios (OR) or risk ratios (RR).
{p 4 8 2}
{cmd:ilevel(}{it:#}{cmd:)} specifies the coverage (eg 90,95,99 percent) for the
individual trial confidence intervals. Default: {cmd:$S_level}.
{cmd:ilevel()} and {cmd:olevel()} need not be the same. See {help set level}.
{p 4 8 2}
{cmd:olevel(}{it:#}{cmd:)} specifies the coverage (eg 90,95,99 percent) for the
overall (pooled) trial confidence intervals. Default: {cmd:$S_level}.
{cmd:ilevel()} and {cmd:olevel()} need not be the same. See {help set level}.
{p 4 8 2}
{cmd:sortby(}{it:varlist}{cmd:)} sorts by variable(s) in {it:varlist}
{p 4 8 2}
{cmd:label([namevar=}{it:namevar}{cmd:], [yearvar=}{it:yearvar}{cmd:])}
labels the data by its name, year or both. Either or both option/s
may be left blank. For the table display the overall length of the
label is restricted to 20 characters. The option {cmd:lcols()} will
override this if invoked.
{p 4 8 2}
{cmd:nokeep} prevents the retention of study parameters in permanent
variables (see saved results below).
{p 4 8 2}
{cmd:notable} prevents display of table of results.
{p 4 8 2}
{cmd:nograph} prevents display of graph.
{p 4 8 2}
{cmd:nosecsub} ({it:v9 update}) prevents the display of sub-estimates
using the second method if {cmd:second()}
is used. Note that this is invoked automatically with user-defined
estimates.
{dlgtab:Forest plot}
{p 4 8 2}
{cmd:effect()} may be used when the effect size and its standard error
are declared. This allows the graph to name the summary statistic used.
{p 4 8 2}
{cmd:nooverall} revents display of overall effect size on graph
(automatically enforces the {cmd:nowt} option).
{p 4 8 2}
{cmd:nowt} prevents display of study weight on the graph.
{p 4 8 2}
{cmd:nostats} prevents display of study statistics on graph.
{p 4 8 2}
{cmd:counts} ({it:v9 update}) displays data counts (n/N) for each group when using
binary data, or the sample size, mean and SD for each group if mean
differences are used (the latter is a new feature).
{p 4 8 2}
{cmd:group1(}{it:string}{cmd:)}, {cmd:group2(}{it:string}{cmd:)} may be
used with the {cmd:counts} option: the text should contain the
names of the two groups.
{p 4 8 2}
{cmd:xlabel()} ({it:v9 update}) defines x-axis labels. This has been modified
so that any number of points may defined. Also, there are no
longer any checks made as to whether these points are sensible, so the
user may define anything if the {cmd:force} option is used. Points must
be comma separated.
{p 4 8 2}
{cmd:xtick()} adds tick marks to the x-axis. Points must
be comma separated.
{p 4 8 2}
{cmd:force} forces the x-axis scale to be in the range specified
by {cmd:xlabel()}.
{p 4 8 2}
{cmd:boxsca()} ({it:v9 update}) controls box scaling.
This has been modified slightly so that the default is 100 (as in a
percentage) and may be increased or decreased as such (e.g., 80 or 120 for
20% smaller or larger respectively)
{p 4 8 2}
{cmd:nobox} prevents a "weighted box" being drawn for each study
and markers for point estimates only are shown.
{p 4 8 2}
{cmd:texts()} ({it:v9 update}) specifies font size for text display on graph.
This has been modified slightly so that the default is 100 (as in a
percentage) and may be increased or decreased as such (e.g., 80 or 120 for
20% smaller or larger respectively)
{dlgtab:Further options for the forest plot in the v9 update}
{p 4 8 2}
{cmd:lcols(}{it:varlist}{cmd:)}, {cmd:rcols(}{it:varlist}{cmd:)}
define columns of additional data to
the left or right of the graph. The first two columns on the right are
automatically set to effect size and weight, unless suppressed using
the options {cmd:nostats} and {cmd:nowt}. If {cmd:counts} is used this
will be set as the third column. {cmd:textsize()} can be used to fine-tune
the size of the text in order to acheive a satisfactory appearance.
The columns are labelled with the variable label, or the variable name
if this is not defined. The first variable specified in {cmd:lcols()} is assumed to be
the study identifier and this is used in the table output.
{p 4 8 2}
{cmd:astext(}{it:#}{cmd:)}
specifies the percentage of the graph to be taken up by text.
The default is 50 and the percentage must be in the range 10-90.
{p 4 8 2}
{cmd:double}
allows variables specified in {cmd:lcols} and {cmd:rcols} to run over two
lines in the plot. This may be of use if long strings are to be used.
{p 4 8 2}
{cmd:nohet}
prevents display of heterogeneity statistics in the graph.
{p 4 8 2}
{cmd:summaryonly}
shows only summary estimates in the graph (may be of use for multiple
subgroup analyses)
{p 4 8 2}
{cmd:rfdist}
displays the confidence interval of the approximate predictive
distribution of a future trial, based on the extent of heterogeneity.
This incorporates uncertainty in the location and spread of the random
effects distribution using the formula {cmd: t(df) x sqrt(se2 + tau2)}
where t is the t-distribution with k-2 degrees of freedom, se2 is the
squared standard error and tau2 the heterogeneity statistic.
The CI is then displayed with lines extending from the diamond. Note that
with <3 studies the distribution is inestimable and effectively infinite, thus
displayed with dotted lines, and where heterogeneity is zero there is still
a slight extension as the t-statistic is always greater than the corresponding
normal deviate. For further information see Higgins JPT, Thompson SG (2006)
{p 4 8 2}
{cmd:rflevel(}{it:#}{cmd:)} specifies the coverage (eg 90,95,99 percent) for the
confidence interval of the predictive distribution. Default: {cmd:$S_level}.
See {help set level}.
{p 4 8 2}
{cmd:null(}{it:#}{cmd:)}
displays the null line at a user-defined value rather than 0 or 1.
{p 4 8 2}
{cmd:nulloff}
removes the null hypothesis line from the graph
{p 4 8 2}
{cmd:favours(}{it:string} # {it:string}{cmd:)}
applies a label saying something about the treatment effect to either
side of the graph (strings are separated by the # symbol). This replaces
the feature available in {cmd:b1title} in the previous version of metan.
{p 4 8 2}
{cmd:firststats(}{it:string}{cmd:)}, {cmd:secondstats(}{it:string}{cmd:)}
labels overall user-defined estimates when these have been specified.
Labels are displayed in the position usually given to the heterogeneity
statistics.
{p 4 8 2}
{cmd:boxopt()}, {cmd:diamopt()}, {cmd:pointopt()}, {cmd:ciopt()}, {cmd:olineopt()}
specify options for the graph routines within the program, allowing the
user to alter the appearance of the graph. Any options associated with a
particular graph command may be used, except some that would cause incorrect
graph appearance. For example, diamonds are plotted using the {help twoway pcspike}
command, so options for line styles are available (see {help line options});
however, altering the x-y
orientation with the option {cmd:horizontal} or {cmd:vertical} is not
allowed. So, {cmd:diamopt(lcolor(green) lwidth(thick))} feeds into a command
such as {cmd:pcspike(y1 x1 y2 x2, lcolor(green) lwidth(thick))}
{p 8 8 2}
{cmd:boxopt()} controls the boxes and uses options for a weighted marker
(e.g., shape, colour; but not size). See {help marker options}
{p 8 8 2}
{cmd:diamopt()} controls the diamonds and uses options for pcspike (not horizontal/vertical).
See {help line options}
{p 8 8 2}
{cmd:pointopt()} controls the point estimate using marker options.
See {help marker options} and {help marker label options}
{p 8 8 2}
{cmd:ciopt()} controls the confidence intervals for studies using options
for pcspike (not horizontal/vertical). See {help line options}
{p 8 8 2}
{cmd:olineopt()} controls the overall effect line with options for an additional
line (not position). See {help line options}
{p 4 8 2}
{cmd:classic} specifies that solid black boxes without point estimate markers are used as
in the previous version of metan.
{p 4 8 2}
{cmd:nowarning} switches off the default display of a note warning that studies are
weighted from random effects anaylses.
{p 4 8 2}
{cmd:dp(}{it:#}{cmd:)} sets the number of decimal places that effect estimates are displayed with
(default is 2).
{p 4 8 2}
{it:graph_options}
specifies overall graph options that would appear at the end of a {cmd:twoway}
graph command. This allows the addition of titles, subtitles, captions etc.,
control of margins, plot regions, graph size, aspect ratio and the use of schemes.
As titles may be added in this way previous options {cmd:b2title} etc. are no
longer necessary. See {search graph options}
{title:Options for labbe}
{p 4 8 2}
{cmd:nowt} declares that the plotted data points are to be the same size.
{p 4 8 2}
{cmd:percent} displays the event rates as percentages rather than proportions.
{p 4 8 2}
{cmd:null} draws a line corresponding to a null effect (ie p1=p2).
{p 4 8 2}
{cmd:or(}{it:#}{cmd:)} draws a line corresponding to a fixed odds ratio of
{it:#}.
{p 4 8 2}
{cmd:rd(}{it:#}{cmd:)} draws a line corresponding to a fixed risk difference of
{it:#}.
{p 4 8 2}
{cmd:rr(}{it:#}{cmd:)} draws a line corresponding to a fixed risk ratio
of {it:#}. See also the {cmd:rrn()} option.
{p 4 8 2}
{cmd:rrn(}{it:#}{cmd:)} draws a line corresponding to a fixed risk ratio
(for the non-event) of {it:#}.
The {cmd:rr()} and {cmd:rrn()} options may require explanation.
Whereas the OR and RD are invariant to the definition of which of
the binary outcomes is the "event" and which is the "non-event",
the RR is not. That is, while the command {cmd:metan a b c d , or}
gives the same result as {cmd:metan b a d c , or} (with direction
changed), an RR analysis does not. The L'Abbe plot allows the display
of either or both to be superimposed risk difference.
{p 4 8 2}
{cmd:logit} is for use with the {cmd:or()} option; it displays the
probabilities on the logit scale ie log(p/1-p). On the logit scale the
odds ratio is a linear effect, and so this makes it easier to assess the
"fit" of the line.
{p 4 8 2}
{cmd:wgt(}{it:weightvar}{cmd:)} specifies alternative weighting by the specified variable
(default is sample size).
{p 4 8 2}
{cmd:symbol(}{it:symbolstyle}{cmd:)} allows the symbol to be changed (see help {help symbolstyle}) the
default being hollow circles (or points if weights are not used).
{p 4 8 2}
{cmd:nolegend} suppresses a legend being displayed (the default if more than one
line corresponding to effect measures are specified).
{p 4 8 2}
{cmd:id(}{it:idvar}{cmd:)} displays marker labels with the specified ID variable {it:idvar}.
{cmd:clockvar()} and {cmd:gap()} may be used to fine-tune the display, which may become
unreadable if studies are clustered together in the graph.
{p 4 8 2}
{cmd:textsize(}{it:#}{cmd:)} increases or decreases the text size of the id label by specifying
{it:#} to be more or less than unity. The default is usually satisfactory, but may need to be adjusted.
{p 4 8 2}
{cmd:clockvar(}{it:clockvar}{cmd:)} specifies the position of {it:idvar} around the
study point, as if it were a clock face (values must be integers- see {help clockposstyle}).
This may be used to organise labels where studies are clustered together. By default, labels are positioned
to the left (9 o'clock) if above the null and to the right (3 o'clock) if below. Missing values
in {it:clockvar} will be assigned the default position, so this need not be specified for all observations.
{p 4 8 2}
{cmd:gap(}{it:#}{cmd:)} increases or decreases the gap between the study marker and the id label by specifying
{it:#} to be more or less than unity. The default is usually satisfactory, but may need to be adjusted.
{p 4 8 2}
{it:graph_options} are options for Stata 8 graphs (see help on {help graph}).
{title:Remarks on metan}
{p 4 4 2}
For two or three variables, a variance-weighted analysis is performed in
a similar fashion to the {help meta} command; the two variable syntax
is {it:theta} and {it:SE(theta)}. The 3 variable syntax is {it:theta},
{it:lower ci (theta)}, {it:upper ci (theta)}. Note that in this situation
"{it:theta}" is taken to be the logarithm of the effect size if the odds
ratio or risk ratio is used. {hi:This differs from the equivalent in the {cmd:meta} command}.
This program does not assume the three variables need log transformation:
if odds ratios or risk ratios are combined, it is up to the user to
log-transform them first. The {cmd:eform} option may be used to change
back to the original scale if needed. By default the confidence
intervals are assumed symmetric, and the studies are pooled by taking
the variance to be equal to (CI width)/2z.
{p 4 4 2}
Note that for graphs on the log scale (that is, ORs or RRs), values
outside the range [10e-8,10e8] are not displayed, and similarly graphs of
other measures (log ORs, RDs, SMDs) are restricted to the range [-10e8,10e8].
A confidence interval which extends beyond this, or the specified scale
if {cmd:force} is used, will have an arrow added at the end of the range.
{p 4 4 2}
{hi: Further notes on v9 update:} If {cmd:by} is used with a string variable the
stratification variable is not sorted alpha-numerically and the original
order that the groups appear in the data is preserved. This may be of use if
a particular display order is required; if not, {cmd:sortby} may be used.
The option {cmd:counts} is now available for continuous data and displays
sample size, mean and SD in each group. The estimate for heterogeneity between
groups from a stratified analysis using the Mantel-Haenszel method, and
arguably the Peto method, is invalid. Therefore this is not displayed in the
output for either of these methods.
{p 4 4 2}
Note that there is a small add-on program to calculate the confidence interval for
the I-squared statistic- type {cmd:i2ci} after running metan. This uses the method
described by Higgins & Thompson (2002), Appendix A2.
{title:Remarks on labbe}
{p 4 4 2}
By default the size of the plotting symbol is proportional to the sample
size of the study. If weights are specified the plotting size will be
proportional to the weight variable. Note that {cmd:labbe} has now been updated to version 8 graphics.
All options work the same as in the previous version, and some minor graphics options have been added.
{title:Stored}
By default, {cmd:metan} adds the following new variables to the data set:
_ES Effect size (ES)
_seES Standard error of ES
or, when OR or RR are specfied:
_selogES the standard error of its logarithm
_LCI Lower confidence limit for ES
_UCI Upper confidence limit for ES
_WT Study percentage weight
_SS Study sample size
{title:Examples}
{p 4 8 2}
All examples use a simulated example dataset (Ross Harris 2006)
{p 8 12 2}
{stata "use http://fmwww.bc.edu/repec/bocode/m/metan_example_data":. use http://fmwww.bc.edu/repec/bocode/m/metan_example_data}
{p 4 8 2}
Risk difference from raw cell counts, random effects model, "label" specification with counts displayed
{p 8 12 2}
{cmd:. metan tdeath tnodeath cdeath cnodeath, }
{p_end}
{p 12 12 2}
{cmd:rd random label(namevar=id, yearid=year) counts}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_basic":click to run})}
{p 4 8 2}
Sort by year, use data columns syntax. Text size increased, specify percentage of graph as text and
two lines per study; suppress stats, weight, heterogeneity stats and table.
{p 8 12 2}
{cmd:. metan tdeath tnodeath cdeath cnodeath, }
{p_end}
{p 12 12 2}
{cmd:sortby(year) lcols(id year country) rcols (population) }
{p_end}
{p 12 12 2}
{cmd:textsize(110) astext(60) double nostats nowt nohet notable}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_cols":click to run})}
{p 4 8 2}
Analyse continuous data (6 parameter syntax), stratify by type of study, with weights summing to 100 within sub group,
second analysis specified, display random effects distribution, show raw data counts, display
"favours treatment vs. favours control" labels
{p 8 12 2}
{cmd:. metan tsample tmean tsd csample cmean csd, }
{p_end}
{p 12 12 2}
{cmd:by(type_study) sgweight fixed second(random) rfdist }
{p_end}
{p 12 12 2}
{cmd:counts label(namevar = id) }
{p_end}
{p 12 12 2}
{cmd:favours(Treatment reduces blood pressure # Treatment increases blood pressure)}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_by":click to run})}
{p 4 8 2}
Generate log odds ratio and standard error, analyse with 2 parameter syntax. Graph has exponential form,
scale is forced within set limits and ticks added, effect label specified.
{p 8 12 2}
{cmd:. gen logor = ln( (tdeath*cnodeath)/(tnodeath*cdeath) )}
{p 8 12 2}
{cmd:. gen selogor = sqrt( (1/tdeath) + (1/tnodeath) + (1/cdeath) + (1/cnodeath) )}
{p 8 12 2}
{cmd:. metan logor selogor, eform xlabel(0.5, 1, 1.5, 2, 2.5) }
{p_end}
{p 12 12 2}
{cmd:force xtick(0.75, 1.25, 1.75, 2.25) effect(Odds ratio)}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_2param":click to run})}
{p 4 8 2}
Display diagnostic test data with 3 parameter syntax. Weight is number of positive diagnoses, axis label set
and null specified at 50%. Overall effect estimate is not displayed, graph for visual examination only.
{p 8 12 2}
{cmd:. metan percent lowerci upperci, wgt(n_positives) }
{p_end}
{p 12 12 2}
{cmd:xlabel(0,10,20,30,40,50,60,70,80,90,100) force }
{p_end}
{p 12 12 2}
{cmd:null(50) label(namevar=id) nooverall notable }
{p_end}
{p 12 12 2}
{cmd:title(Sensitivity, position(6))}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_diag":click to run})}
{p 4 8 2}
User has analysed data with a non-standard technique and supplied effect estimates, weights and description of statistics.
The scheme "Economist" has been used.
{p 8 12 2}
{cmd:. metan OR ORlci ORuci, wgt(bweight) }
{p_end}
{p 12 12 2}
{cmd:first(0.924 0.753 1.095 Bayesian) }
{p_end}
{p 12 12 2}
{cmd:firststats(param V=3.86, p=0.012) }
{p_end}
{p 12 12 2}
{cmd:label(namevar=id) }
{p_end}
{p 12 12 2}
{cmd:xlabel(0.25, 0.5, 1, 2, 4) force }
{p_end}
{p 12 12 2}
{cmd:null(1) aspect(1.2) scheme(economist)}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_user":click to run})}
{p 4 8 2}
Variable "counts" defined showing raw data. Options to change the box, effect estimate marker and confidence interval used,
and the counts variable has been attached to the estimate marker as a label.
{p 8 12 2}
{cmd:. gen counts = ". " + string(tdeath) + "/" + string(tdeath+tnodeath) }
{p_end}
{p 12 12 2}
{cmd:+ ", " + string(cdeath) + "/" + string(cdeath+cnodeath)}
{p 8 12 2}
{cmd: . metan tdeath tnodeath cdeath cnodeath, }
{p_end}
{p 12 12 2}
{cmd:lcols(id year) notable }
{p_end}
{p 12 12 2}
{cmd:boxopt( mcolor(forest_green) msymbol(triangle) ) }
{p_end}
{p 12 12 2}
{cmd:pointopt( msymbol(triangle) mcolor(gold) msize(tiny) }
{p_end}
{p 12 12 2}
{cmd:mlabel(counts) mlabsize(vsmall) mlabcolor(forest_green) mlabposition(1) ) }
{p_end}
{p 12 12 2}
{cmd:ciopt( lcolor(sienna) lwidth(medium) )}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples metan_example_custom":click to run})}
{p 4 8 2}
L'Abbe plot with labelled axes and display of risk ratio and risk difference.
{p 8 12 2}
{cmd:. labbe tdeath tnodeath cdeath cnodeath, }
{p_end}
{p 12 12 2}
{cmd:xlabel(0,0.25,0.5,0.75,1) ylabel(0,0.25,0.5,0.75,1) }
{p_end}
{p 12 12 2}
{cmd:rr(1.029) rd(0.014) null}
{p_end}
{p 12 12 2}
{it:({stata "metan_examples labbe_example":click to run})}
{title:Authors}
{p 4 4 0}
Michael J Bradburn, Jonathan J Deeks, Douglas G Altman.
Centre for Statistics in Medicine, University of Oxford,
Wolfson College Annexe, Linton Road, Oxford, OX2 6UD, UK
{title:Version 9 update}
{p 4 4 0}
Ross J Harris, Roger M Harbord, Jonathan A C Sterne.
Department of Social Medicine, University of Bristol,
Canynge Hall, Whiteladies Road, Bristol BS8 2PR, UK
{title:Other updates and improvements to code and help file}
{p 4 4 0}
Patrick Royston. MRC Clinical Trials Unit, 222 Euston Road,
London, NW1 2DA
{title:Acknowledgements}
{p 4 4 0}
Thanks to Vince Wiggins, Kit Baum and Jeff Pitblado of Statacorp
who offered advice and helped facilitate the version 9 update.
Thanks also to all the people who helped with beta-testing and
made comments and suggested improvements.
{title:References}
{p 4 4 0}
Higgins JPT, Thompson SG, Deeks JJ,
Altman DG. Measuring inconsistency in meta-analyses. BMJ
2003; 327:557-560. {browse "http://dx.doi.org/10.1136/bmj.327.7414.557":http://dx.doi.org/10.1136/bmj.327.7414.557}
{p 4 4 0}
Higgins JPT, Thompson SG (2006) Presenting random effects meta-analyses:
where we are going wrong? (from presentation, work in preparation)
{title:Also see}
STB: STB-44 sbe24
On-line: help for {help metan7}, {help metannt}
{help meta} (if installed), {help metacum} (if installed),
{help metareg} (if installed), {help metabias} (if installed),
{help metatrim} (if installed), {help metainf} (if installed),
{help galbr} (if installed), {help metafunnel} (if installed)

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/*
*! metan7 dialog version 1.0.5, 1 Apr 2004, T. J. Steichen, steichen@triad.rr.com
*! for metan7 version 1.85, 15 Mar 2004, M. J. Bradburn, mike.bradburn@cancer.org.uk
Fixed and random effects meta-analysis
--------------------------------------
Syntax: metan7 varlist [if exp] [in range] [,
label(namevar=name, yearvar=year)
fixed random fixedi randomi peto
rr or rd cornfield chi2 breslow notable nograph
cohen hedges glass nostandard
log sortby(sort_vars) ilevel(#) olevel(#) nokeep
xlabel(#,..,#) force t1title(#,..,#) boxsha(#) boxsca(#) texts(#)
saving(filename) nowt counts nostats nooverall
group1(text) group2(text) effect(text) nobox eform
wgt(weightvar) xtick(#,..,#)
t2title(#,..,#) b1title(#,..,#) b2title(#,..,#)
nointeger cc(#) by(byvar) nosubgroup sgweight legend(text) ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-analysis of Binary and Continuous Data (meta&n)" "db metan7"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_medium
INCLUDE _ht300
DEFINE _dlgwd 450
INCLUDE header
HELP hlp1, view("help metan7")
RESET res1, label("Reset")
DIALOG main, label("metan7 1.85 - Meta-analysis of Binary & Continuous") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 310 _ht1h, ///
label("Type of Data:")
RADIO r_binary 15 25 60 ., ///
label(" Count") first ///
onclickon(script main_binary_on) ///
onclickoff(script main_binary_off)
RADIO r_continuous 75 25 90 ., ///
label("Continuous") middle ///
onclickon(script main_continuous_on) ///
onclickoff(script main_continuous_off)
RADIO r_effect_ci 165 25 75 ., ///
label("Effect/CI") middle ///
onclickon(script main_effect_ci_on) ///
onclickoff(script main_effect_ci.off)
RADIO r_effect_se 240 25 75 ., ///
label("Effect/SE") last ///
onclickon(script main_effect_se_on) ///
onclickoff(script main_effect_se_off)
/* end GROUPBOX gb_data */
TEXT tx_binary 10 50 310 ., ///
label("Vars for Counts: a, b, c, d, in that order")
VARLIST vl_binary @ _ss @ ., ///
label("Vars for a, b, c, d")
TEXT tx_contin_exp @ 50 310 ., ///
label("Vars for Exp. Group: n, mean, sd, in that order")
VARLIST vl_contin_exp @ _ss @ ., ///
label("Experimental Group: n, mean, sd")
TEXT tx_contin_ctl @ _ss 310 ., ///
label("Vars for Control Group: n, mean, sd, in that order")
VARLIST vl_contin_ctl @ _ss @ ., ///
label("Control Group: n, mean, sd")
TEXT tx_effect_ci @ 50 310 ., ///
label("Vars for Effects: theta, lowerCI, upperCI, in that order")
VARLIST vl_effect_ci @ _ss @ ., ///
label("Effect Sizes: theta, lowerCI, upperCI")
TEXT tx_effect_se @ 50 310 ., ///
label("Vars for Effects: theta, se(theta), in that order")
VARLIST vl_effect_se @ _ss @ ., ///
label("Effect Sizes: theta, se(theta)")
/* end vars */
GROUPBOX gb_labels 10 130 310 _ht3h, ///
label("Labels for Data:")
CHECKBOX cb_name 20 150 70 ., ///
label("Name:") ///
onclickon(main.vn_name.enable) ///
onclickoff(main.vn_name.disable)
VARNAME vn_name 100 150 210 ., ///
label("Name Variable")
CHECKBOX cb_year 20 170 70 ., ///
label("Year:") ///
onclickon(main.vn_year.enable) ///
onclickoff(main.vn_year.disable)
VARNAME vn_year 100 170 210 ., ///
label("Year Variable")
/* end GROUPBOX gb_labels */
GROUPBOX gb_by 10 200 310 45, ///
label("By Variable:")
CHECKBOX cb_by 20 220 70 ., ///
label(" By:") ///
onclickon(script main_by_on) ///
onclickoff(script main_by_off)
VARLIST vl_by 100 220 210 ., ///
label("By Variable") ///
option("by")
/* end GROUPBOX gb_by */
GROUPBOX gb_sortby 10 250 310 45, ///
label("Sort Data:")
CHECKBOX cb_sortby 20 270 70 ., ///
label(" By:") ///
onclickon(main.vl_sortby.enable) ///
onclickoff(main.vl_sortby.disable)
VARLIST vl_sortby 100 270 210 ., ///
label("Name Variable") ///
option("sortby")
/* end GROUPBOX gb_sortby */
GROUPBOX gb_opts 325 5 120 85, ///
label("General Options:")
CHECKBOX cb_nokeep 330 25 70 ., ///
label("noKeep") ///
option("nokeep")
CHECKBOX cb_ilevel 330 45 60 ., ///
label("ilevel:") ///
onclickon(main.ed_ilevel.enable) ///
onclickoff(main.ed_ilevel.disable)
EDIT ed_ilevel 400 45 40 ., ///
label("ilevel") ///
numonly default(global S_level) ///
option("ilevel")
CHECKBOX cb_olevel 330 65 60 ., ///
label("olevel:") ///
onclickon(main.ed_olevel.enable) ///
onclickoff(main.ed_olevel.disable)
EDIT ed_olevel 400 65 40 ., ///
label("olevel") ///
numonly default(global S_level) ///
option("olevel")
/* end GROUPBOX gb_opts */
FRAME fr_wgt 325 95 120 60
CHECKBOX cb_wgt 330 100 110 ., ///
label("Weight Var") ///
onclickon(main.vn_wgt.enable) ///
onclickoff(main.vn_wgt.disable)
VARNAME vn_wgt 330 121 110 ., ///
label("Weight Variable") ///
option("wgt")
/* end wgt */
GROUPBOX gb_by_opts 325 200 120 61, ///
label("By Options:")
CHECKBOX cb_nosubgroup 330 220 100 ., ///
label("noSubGroup") ///
option("nosubgroup")
CHECKBOX cb_sgweight 330 240 100 ., ///
label("sgWeight") ///
option("sgweight")
/* end GROUPBOX gb_by_opts */
END
DIALOG metan7_b, tabtitle("Binary...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht9h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Mantel-Haenszel") first ///
onclickoff(program metan7_b_bd) ///
option("fixed")
RADIO r_fixedi @ _ss @ ., ///
label("Fixed, Inverse Variance") middle ///
onclickoff(program metan7_b_bd) ///
option("fixedi")
RADIO r_random @ _ss 195 ., ///
label("Random (M-H heterogeneity)") middle ///
onclickon(script metan7_b_random_on) ///
onclickoff(program metan7_b_chi2) ///
option("random")
RADIO r_randomi @ _ss @ ., ///
label("Random (I-V heterogeneity)") middle ///
onclickon(script metan7_b_random_on) ///
onclickoff(program metan7_b_chi2) ///
option("randomi")
RADIO r_peto @ _ss @ ., ///
label("Peto") last ///
onclickon(script metan7_b_peto_on) ///
onclickoff(program metan7_b_cornfield) ///
option("peto")
GROUPBOX gb_stat 220 10 120 _ht5h, ///
label("Statistic")
RADIO r_rr 230 30 80 ., ///
label("RR") first ///
onclickon(script metan7_b_rr_on) ///
onclickoff(program metan7_b_rr_off) ///
option("rr")
RADIO r_or @ _ss @ ., ///
label("OR") middle ///
onclickon(program metan7_b_or_on) ///
onclickoff(program metan7_b_or_off) ///
option("or")
RADIO r_rd @ _ss @ ., ///
label("RD") last ///
option("rd")
CHECKBOX cb_cornfield 10 140 200 ., ///
label("Use Cornfield CI's") ///
option("cornfield")
CHECKBOX cb_chi2 10 160 200 ., ///
label("Use Chi-2 Statistic") ///
option("chi2")
CHECKBOX cb_breslow 10 180 200 ., ///
label("Use Breslow-Day test") ///
option("breslow")
CHECKBOX cb_log 230 100 200 ., ///
label("Log scaled") ///
option("log")
CHECKBOX cb_eform 230 120 200 ., ///
label("Exponentiate") ///
option("eform")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_cc 10 260 150 ., ///
label("Continuity Correction:") ///
onclickon(metan7_b.ed_cc.enable) ///
onclickoff(metan7_b.ed_cc.disable)
EDIT ed_cc 160 260 65 ., ///
label("Continuity Correction") ///
option("cc")
CHECKBOX cb_nointeger 230 260 200 ., ///
label("noInteger") ///
option("nointeger")
END
DIALOG metan7_c, tabtitle("Continuous...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random (I-V heterogeneity)") last ///
option("random")
GROUPBOX gb_stat 220 10 120 _ht7h, ///
label("Statistic")
RADIO r_cohen 230 30 100 ., ///
label("Cohen") first ///
option("cohen")
RADIO r_hedges @ _ss @ ., ///
label("Hedges") middle ///
option("hedges")
RADIO r_glass @ _ss @ ., ///
label("Glass") middle ///
option("glass")
RADIO r_nostandard @ _ss @ ., ///
label("noStandard") last ///
option("nostandard")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_nointeger 230 260 200 ., ///
label("noInteger") ///
option("nointeger")
END
DIALOG metan7_e, tabtitle("Effect...")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random (I-V heterogeneity)") last ///
option("random")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
CHECKBOX cb_effect 10 110 100 ., ///
label("Effect Label:") ///
onclickon(metan7_e.ed_effect.enable) ///
onclickoff(metan7_e.ed_effect.disable)
EDIT ed_effect 115 @ 225 ., ///
label("effect label") ///
option("effect")
END
DIALOG graph, tabtitle("Graph Opts")
BEGIN
CHECKBOX cb_saving 10 10 100 ., ///
label("Save graph:") ///
onclickon(graph.fi_saving.enable) ///
onclickoff(graph.fi_saving.disable)
FILE fi_saving 110 10 240 ., ///
label("File Name") ///
error("Save filename") ///
dialogtitle("Save graph") ///
filter("Stata Graphs|*.gph") ///
save ///
option("saving")
/* end saving */
GROUPBOX gb_xlabel 10 40 330 _ht3h, ///
label("X-axis Tick Labels (comma between):")
CHECKBOX cb_xlabel 15 60 45 ., ///
label("Set:") ///
onclickon(script graph_xlabel_on) ///
onclickoff(script graph_xlabel_off)
EDIT ed_xlabel 65 @ 270 ., ///
label("X Tick labels") ///
option("xlabel")
CHECKBOX cb_force 15 80 320 ., ///
label("Force Scale to Tick Range") ///
option("force")
/* end tick labels */
CHECKBOX cb_counts 15 115 65 ., ///
label("Counts") ///
onclickon(script graph_counts_on) ///
onclickoff(script graph_counts_off) ///
option("counts")
CHECKBOX cb_nowt 15 145 85 ., ///
label("noWeights") ///
option("nowt")
CHECKBOX cb_nostats 15 165 85 ., ///
label("noStats") ///
option("nostats")
CHECKBOX cb_nooverall 115 145 85 ., ///
label("noOverall") ///
option("nooverall")
CHECKBOX cb_nobox 115 165 85 ., ///
label("noBox") ///
option("nobox")
/* end switches */
CHECKBOX cb_legend 115 185 70 ., ///
label("Legend:") ///
onclickon(graph.ed_legend.enable) ///
onclickoff(graph.ed_legend.disable)
EDIT ed_legend 190 @ 55 ., ///
label("legend") ///
option("legend")
CHECKBOX cb_grp1 85 115 55 ., ///
label("Grp 1:") ///
onclickon(graph.ed_grp1.enable) ///
onclickoff(graph.ed_grp1.disable)
EDIT ed_grp1 145 @ 90 ., ///
label("Grp1") ///
option("group1")
CHECKBOX cb_grp2 245 115 55 ., ///
label("Grp 2:") ///
onclickon(graph.ed_grp2.enable) ///
onclickoff(graph.ed_grp2.disable)
EDIT ed_grp2 305 @ 90 ., ///
label("Grp2") ///
option("group2")
CHECKBOX cb_boxy 255 145 95 ., ///
label("Box yscale:") ///
onclickon(graph.ed_boxy.enable) ///
onclickoff(graph.ed_boxy.disable)
EDIT ed_boxy 355 @ 40 ., ///
label("Box yscale") numonly default(1) ///
option("boxsca")
CHECKBOX cb_boxs 255 165 95 ., ///
label("Box shade:") ///
onclickon(graph.ed_boxs.enable) ///
onclickoff(graph.ed_boxs.disable)
EDIT ed_boxs 355 @ 40 ., ///
label("Box shading") numonly default(4) ///
option("boxsha")
CHECKBOX cb_texts 255 185 95 ., ///
label("Font scale:") ///
onclickon(graph.ed_texts.enable) ///
onclickoff(graph.ed_texts.disable)
EDIT ed_texts 355 @ 40 ., ///
label("Font scale") numonly default(1) ///
option("texts")
/* end sets */
GROUPBOX gb_gopts7 10 205 390 _ht1h, ///
label("Allowed Graph7 Options:")
EDIT ed_gopts7 15 225 380 ., ///
label("Graph7 Options")
CHECKBOX cb_b1 15 255 390 ., ///
label("Effect Direction Labels, option b1( ):") ///
onclickon(script graph_b1_on) ///
onclickoff(script graph_b1_off)
TEXT tx_b1_l 10 275 25 ., ///
label("Left:")
EDIT ed_b1_l 35 275 160 ., ///
label(" Left")
TEXT tx_b1_r 205 275 35 ., ///
label("Right:")
EDIT ed_b1_r 240 275 160 ., ///
label("Right")
/* end Graph7 opts */
END
INCLUDE ifin
SCRIPT main_binary_on
BEGIN
main.tx_binary.show
main.vl_binary.show
main.tx_binary.enable
main.vl_binary.enable
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
graph.cb_counts.enable
END
SCRIPT main_binary_off
BEGIN
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
graph.cb_counts.disable
END
SCRIPT main_continuous_on
BEGIN
main.tx_contin_exp.show
main.vl_contin_exp.show
main.tx_contin_exp.enable
main.vl_contin_exp.enable
main.tx_contin_ctl.show
main.vl_contin_ctl.show
main.tx_contin_ctl.enable
main.vl_contin_ctl.enable
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
END
SCRIPT main_continuous_off
BEGIN
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_ci_on
BEGIN
main.tx_effect_ci.show
main.vl_effect_ci.show
main.tx_effect_ci.enable
main.vl_effect_ci.enable
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_ci_off
BEGIN
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
END
SCRIPT main_effect_se_on
BEGIN
main.tx_effect_se.show
main.vl_effect_se.show
main.tx_effect_se.enable
main.vl_effect_se.enable
main.tx_effect_ci.disable
main.vl_effect_ci.disable
main.tx_effect_ci.hide
main.vl_effect_ci.hide
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_se_off
BEGIN
main.tx_effect_se.disable
main.vl_effect_se.disable
main.tx_effect_se.hide
main.vl_effect_se.hide
END
SCRIPT main_by_on
BEGIN
main.vl_by.enable
main.gb_by_opts.enable
main.cb_nosubgroup.enable
main.cb_sgweight.enable
END
SCRIPT main_by_off
BEGIN
main.vl_by.disable
main.gb_by_opts.disable
main.cb_nosubgroup.disable
main.cb_sgweight.disable
END
SCRIPT metan7_b_random_on
BEGIN
metan7_b.cb_breslow.disable
metan7_b.cb_chi2.disable
END
SCRIPT metan7_b_peto_on
BEGIN
metan7_b.cb_breslow.disable
metan7_b.cb_cornfield.disable
END
PROGRAM metan7_b_bd
BEGIN
if metan7_b.r_or {
call metan7_b.cb_breslow.enable
}
END
PROGRAM metan7_b_chi2
BEGIN
if metan7_b.r_or & !(metan7_b.r_random | metan7_b.r_randomi) {
call metan7_b.cb_chi2.enable
}
END
PROGRAM metan7_b_cornfield
BEGIN
if metan7_b.r_or {
call metan7_b.cb_cornfield.enable
}
END
SCRIPT metan7_b_rr_on
BEGIN
metan7_b.cb_log.enable
metan7_b.cb_eform.enable
END
PROGRAM metan7_b_or_on
BEGIN
call metan7_b.cb_log.enable
call metan7_b.cb_eform.enable
call metan7_b.r_peto.enable
if !metan7_b.r_peto {
call metan7_b.cb_cornfield.enable
}
if !(metan7_b.r_random | metan7_b.r_randomi) {
call metan7_b.cb_chi2.enable
}
call metan7_b.cb_breslow.enable
END
PROGRAM metan7_b_or_off
BEGIN
if !metan7_b.r_rr {
call metan7_b.cb_log.disable
call metan7_b.cb_eform.disable
}
call metan7_b.r_peto.disable
call metan7_b.cb_cornfield.disable
call metan7_b.cb_chi2.disable
call metan7_b.cb_breslow.disable
END
PROGRAM metan7_b_rr_off
BEGIN
if !metan7_b.r_or {
call metan7_b.cb_log.disable
call metan7_b.cb_eform.disable
}
END
SCRIPT graph_xlabel_on
BEGIN
graph.ed_xlabel.enable
graph.cb_force.enable
END
SCRIPT graph_xlabel_off
BEGIN
graph.ed_xlabel.disable
graph.cb_force.disable
END
SCRIPT graph_counts_on
BEGIN
graph.cb_grp1.enable
graph.cb_grp2.enable
END
SCRIPT graph_counts_off
BEGIN
graph.cb_grp1.disable
graph.cb_grp2.disable
graph.cb_grp1.setoff
graph.cb_grp2.setoff
graph.ed_grp1.disable
graph.ed_grp2.disable
END
SCRIPT graph_b1_on
BEGIN
graph.tx_b1_l.enable
graph.ed_b1_l.enable
graph.tx_b1_r.enable
graph.ed_b1_r.enable
END
SCRIPT graph_b1_off
BEGIN
graph.tx_b1_l.disable
graph.ed_b1_l.disable
graph.tx_b1_r.disable
graph.ed_b1_r.disable
END
PROGRAM command
BEGIN
put " metan7 "
if main.r_binary {
varlist main.vl_binary
}
if main.r_continuous {
varlist main.vl_contin_exp main.vl_contin_ctl
}
if main.r_effect_ci {
varlist main.vl_effect_ci
}
if main.r_effect_se {
varlist main.vl_effect_se
}
INCLUDE _ifin_pr
beginoptions
if main.cb_name | main.cb_year {
put "label("
if main.cb_name {
put "namevar=" main.vn_name
}
if main.cb_name & main.cb_year {
put ", "
}
if main.cb_year {
put "yearvar=" main.vn_year
}
put ") "
}
optionarg main.vl_sortby
optionarg main.vl_by
optionarg main.ed_ilevel
optionarg main.ed_olevel
optionarg main.vn_wgt
option main.cb_nokeep
option main.cb_nosubgroup
option main.cb_sgweight
if main.r_binary {
if !main.vn_wgt {
option radio(metan7_b r_fixed r_fixedi r_random r_randomi r_peto)
}
option radio(metan7_b r_rr r_or r_rd)
option metan7_b.cb_cornfield
option metan7_b.cb_chi2
option metan7_b.cb_breslow
option metan7_b.cb_log
option metan7_b.cb_eform
option metan7_b.cb_nograph
option metan7_b.cb_notable
optionarg metan7_b.ed_cc
option metan7_b.cb_nointeger
}
if main.r_continuous {
if !main.vn_wgt {
option radio(metan7_c r_fixed r_random)
}
option radio(metan7_c r_cohen r_hedges r_glass r_nostandard)
option metan7_c.cb_nograph
option metan7_c.cb_notable
option metan7_c.cb_nointeger
}
if main.r_effect_se | main.r_effect_ci {
if !main.vn_wgt {
option radio(metan7_e r_fixed r_random)
}
optionarg metan7_e.ed_effect
option metan7_e.cb_nograph
option metan7_e.cb_notable
}
optionarg graph.fi_saving
optionarg graph.ed_xlabel
option graph.cb_force
option graph.cb_counts
option graph.cb_nowt
option graph.cb_nostats
option graph.cb_nooverall
option graph.cb_nobox
optionarg graph.ed_grp1
optionarg graph.ed_grp2
optionarg graph.ed_texts
optionarg graph.ed_legend
optionarg graph.ed_boxy
optionarg graph.ed_boxs
put graph.ed_gopts7 " "
if graph.cb_b1 {
put "b1(*I:" graph.ed_b1_l "*" graph.ed_b1_r ") "
}
endoptions
END

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.-
help for ^metan7^, ^labbe^, ^funnel^
.-
Fixed and random effects meta-analysis
--------------------------------------
^metan7^ varlist [^if^ exp] [^in^ range]
[, ^rr or rd cohen hedges glass nosta^ndard
^fixed random fixedi randomi peto wgt(^weightvar^) nooverall^
^cornfield chi2 breslow log eform noint^eger^ cc(^#^)^
^by(^byvar^) nosubgroup sgweight ilevel(^#^) olevel(^#^)^
^label(namevar=^name^, yearvar=^year^) legend(^text^)^
^sortby(^sort_vars^) nokeep notable nograph^
^xt^ick^(^#,..,#^) xla^bel^(^#,..,#^) force^
^t1title(^#,..,#^) t2title(^#,..,#^) b1title(^#,..,#^) b2title(^#,..,#^)^
^boxsha(^#^) boxsca(^#^) nobox texts(^#^) nowt nostats counts^
^group1(^text^) group2(^text^) effect(^text^) saving(^filename^)^ ]
^labbe^ varlist [^if^ exp] [^in^ range] [^weight^]
[ , ^nowt per^cent ^or(^#^) rr(^#^) rd(^#^) rrn(^#^) null logit^
graph_options ]
^funnel^ [varlist] [^if^ exp] [^in^ range] [^weight^]
[, ^ysq^rt ^sa^mple ^ov^erall(#) graph_options ]
Description
-----------
These routines provide methods for the meta-analysis of aggregate level
data. Either binary (event) or continuous data from two groups may be
combined using the ^metan7^ command. Additionally, pre-calculated effect
sizes may be pooled. Several meta-analytic methods are available, and
the results may be displayed graphically in a Forest plot. A test for
the statistical significance of the overall effect size is also provided,
along with a test for excess between-trial variation (heterogeneity)
and the I-squared measure which is an alternative summary of this
(Higgins & Thompson 2002). Note that this command has been updated-
see ^metan^
^labbe^ draws a L'Abbe plot for event data (proportion of successes in the
two groups). This is an alternative to the graph produced by ^metan7^
^funnel^ may be used for producing a "funnel plot", a graph of either the
study sample size, standard error or precision (inverse of s.e.) against
the effect size.
Options for ^metan7^
-----------------
The main meta-analysis routine ^metan7^ requires either two, three, four
or six variables to be declared.
When four variables are specified, analysis of binary data is performed
on the 2x2 table with cell counts denoted by the variable list.
With six variables, the data are assumed continuous and to be the sample
size, mean and standard deviation of the experimental group followed by
those of the control group.
For two or three variables, a variance-weighted analysis is performed in
a similar fashion to the @meta@ command;
the two variable syntax is <theta> and <SE(theta)>.
the 3 variable syntax is <theta>, <lower ci (theta)>, <upper ci (theta)>
Note that in this situation "theta" is taken to be the logarithm of the
effect size if the odds ratio or risk ratio is used. ^This differs from^
^the equivalent in the meta command^. This program does not assume the
three variables need log transformation: if odds ratios or risk ratios
are combines, it is up to the user to log-transform them first. The
^eform^ option can be used to change back to the original scale if needed.
By default the confidence intervals are assumed symmetric, and the studies
are pooled by taking the variance to be equal to (CI width)/2z.
Specifying the measure and model with which to pool the data
------------------------------------------------------------
Options for binary data (4 variables)
^rr^ pools risk ratios [default]
^or^ pools odds ratios
^rd^ pools risk differences
^fixed^ specifies a fixed effect model using the method of
Mantel and Haenszel [default]
^fixedi^ specifies a fixed effect model using the inverse variance method
^peto^ specifies that Peto's method is used to pool odds ratios
^random^ specifies a random effects model using the method of
DerSimonian & Laird, with the estimate of heterogeneity being taken
from the Mantel-Haenszel model
^randomi^ specifies a random effects model using the method of
DerSimonian & Laird, with the estimate of heterogeneity being taken
from the inverse variance fixed effect model
^cornfield^ computes confidence intervals for odds ratios by method of
Cornfield, rather than the (default) Woolf method
^chi2^ displays chi-squared statistic (instead of z) for the test
of significance of the pooled effect size. This is available only for
odds ratios pooled using Peto or Mantel-Haenszel methods
^breslow^ produces Breslow-Day test for homogeneity of ORs
^cc(^#^)^ defines a fixed continuity correction to add in the case where
a study contains a zero cell. By default, metan7 adds 0.5 to each cell of
a trial where a zero is encountered when using Inverse-Variance,
Der-Simonian & Laird or Mantel-Haenszel weighting to enable finite
variance estimators to be derived. However, the cc option allows the use
of other constants (including none). See also the ^nointeger^ option below.
^nointeger^ allows the cell counts to be non-integers. This may be useful
when a variable continuity correction is sought for studies containing
zero cells, but also may be used in other circumstances, such as where a
cluster-randomised trial is to be incorporated and the "effective sample
size" is less than the total number of observations.
Options for continuous data
^cohen^ pools standardised mean differences by the method of Cohen
[default]
^hedges^ pools standardised mean differences by the method of Hedges
^glass^ pools standardised mean differences by the method of Glass
^nostandard^ pools unstandardised mean differences
^fixed^ specifies a fixed effect model using the inverse variance method
[default]
^random^ specifies a random effects model using the DerSimonian & Laird
method
^nointeger^ denotes that the number of observations in each arm does not
need to be an integer. By default, the first and fourth variables specified
(containing N_intervention and N_control respectively) may occasionally be
non-integer (see above entry under binary data)
Alternative weighting (for all data types)
^wgt(^wgtvar^)^ denotes that the effect size is to be computed by assigning
a weight of wgtvar to the studies. When RRs or ORs are declared, their
logarithms are weighted. You should only use this option if you are
satisfied that the weights are meaningful.
Output
------
General
^by^ specifies that the meta-analysis is to be stratified
according to the variable declared.
^sgweight^ specifies that the display is to present the percentage
weights within each subgroup separately. By default metan7 presents
weights as a percentage of the overall total.
^nosubgroup^ indicates that no within-group results are to be
presented. By default metan7 pools trials both within and across
all studies
^log^ reports the results on the log scale
(valid for OR and RR analyses only)
^eform^ exponentiates all effect sizes and confidence intervals
(valid only for analyses of odds ratios or risk ratios)
^ilevel()^ specifies the significance level (eg 90,95,99) for the
individual trial confidence intervals
^olevel()^ specifies the significance level (eg 90,95,99) for the
overall (pooled) confidence intervals
^ilevel^ and ^olevel^ need not be the same, and by default are equal
to the significance level specified using the ^set level^ command
^sortby(^varlist^)^ sorts by one or more variables ^varlist^
^label(^ [^namevar^=namevar] [^,yearvar^=yearvar] ^)^
labels the data by its name, year or both. Either or both option/s
may be left blank. For the table display the overall length of the
label is restricted to 20 characters
^nokeep^ prevents the retention of study parameters in permanent
variables (see saved results below)
^notable^ prevents display of table of results
^nograph^ prevents display of graph
Graph display options for forest plot
^legend()^ specifies a label to be used for the study identifiers at
the top of the graph. This is a rename of the ^stext()^ option that
appeared in some previous versions of metan7.
^effect()^ may be used when the effect size and its standard error
are declared. This allows the graph to name the summary statistic used
^nooverall^ prevents display of overall effect size on graph
(automatically enforces the ^nowt^ option)
^nowt^ prevents display of study weight on graph
^nostats^ prevents display of study statistics on graph
^counts^ displays data counts (n/N) for each group when using
binary data
^group1()^, ^group2()^ can be used with the counts option: the text should
contain the names of the two groups.
^xlabel()^ defines x-axis labels
^xtick()^ adds tick marks to the x-axis
^force()^ forces the x-axis scale to be in the range specified
by ^xlabel()^
^t1title(..)^, ^t2title(..)^, ^b1title(..)^, ^b2title(..)^
adds titles to graph in the usual manner, but ^b1title(..)^ has an added
feature. If the text starts with "*I:" and contains an asterisk, metan7
interprets this as meaning the label declares something about the effect
sizes. For example,
. ^metan7^ [varlist] ^, b1title(*I:^Favours treatment ^*^ Favours control^)^
displays a legend under the graph denoting that studies with effect sizes
to the left of the graph (eg below odds ratios of less than 1) denote a
beneficial treatment, whilst those finding effects to the right indicate
a negative treatment effect.
^boxsha()^ controls box shading intensity, between 0 and 4. The default
is 4, which produces a filled box
^boxsca()^ controls box scaling, which by default is 1
^nobox^ prevents a "weighted box" being drawn for each study, instead
displaying effect sizes as identically sized circles
^texts()^ specifies font size for text display on graph. The default
size is 1
^saving(^filename^)^ saves the forest plot to the specified file
Note that for graphs on the log scale (that is, ORs or RRs), values
outside the range [10e-8,10e8] are not displayed. A confidence interval
which extends beyond this will have an arrow added at the end of the range;
should the effect size and confidence interval be completely off this
scale, they will be represented as a single (triangle-shaped) arrow.
By default, ^metan7^ adds the following new variables to the data set:
^_ES^ Effect size (ES)
^_seES^ Standard error of ES
or, when OR or RR are specfied:
^_selogES^ the standard error of its logarithm
^_LCI^ Lower confidence limit for ES
^_UCI^ Upper confidence limit for ES
^_WT^ Study percentage weight
^_SS^ Study sample size
Options for ^funnel^
------------------
If the ^funnel^ command is invoked following ^metan7^ with no parameters
specified it will produce a standard funnel plot of precision (1/SE)
against treatment effect. Addition of the ^noinvert^ option will produce
a plot of standard error against treatment effect. The alternative
sample size version of the funnel plot can be obtained by using the
^sample^ option (this automatically selects the ^noinvert^ option).
Alternative plots can be created by specifying ^precision_var^ and
^effect_size^. If the effect size is a relative risk or odds ratio,
the ^xlog^ option should be used to create a symmetrical plot.
All options for graph are valid. Additionally,
^sample^ denotes that the y-axis is the sample size and not a
standard error
^noinvert^ prevents the values of the precision variable from being
inverted
^ysqrt^ represent y-axis on square root scale
^overall(^x^)^ draws a dashed vertical line at overall effect size given by x
Options for ^labbe^
-----------------
By default the size of the plotting symbol is proportional to the sample
size of the study. If weight is specified the plotting size will be
proportional to the weight variable.
All options for graph are valid. Additionally, the following options may
be used
^nowt^ declares that the plotted data points are to be the same size
^percent^ display the event rates as percentages rather than proportions
^null^ draws a line corresponding to a null effect (ie p1=p2)
^or(^x1^)^ draws a line corresponding to a fixed odds ratio of x1
^rd(^x2^)^ draws a line corresponding to a fixed risk difference of x2
^rr(^x3^)^ draws a line corresponding to a fixed risk ratio
(for the event) of x3
^rrn(^x4^)^ draws a line corresponding to a fixed risk ratio
(for the non-event) of x4
The latter two may require explanation: whereas the OR and RD are
invariant to the definition of which of the binary outcomes is the "event"
and which is the "non-event", the RR is not. That is, whilst the command
^metan7 a b c d , or^ gives the same result as ^metan7 b a d c , or^
(with direction changed), an RR analysis does not. The L'Abbe plot allows
the display of either or both be superimposed
^logit^ is for use when the ^or()^ option has been used; it displays the
probabilities on the logit scale ie log(p/1-p). On the logit scale the
odds ratio is a linear effect, and so this makes it easier to assess the
"fit" of the line.
One note of caution: depending on the size of the studies, you may need to
rescale the graph (by means of the ^psize()^ option)
Examples
--------
. ^metan7 tdeath tnodeath cdeath cnodeath, or chi2 label(namevar=trialid)^
. ^metan7 tdeath tnodeath cdeath cnodeath, rd random^
. ^metan7 tdeath tnodeath cdeath cnodeath, olevel(99) ilevel(95)^ /*
/* ^sortby(year) label(namevar=trialid, yearid=year) nostats nowt^
. ^metan7 n1 mean1 sd1 n2 mean2 sd2, by(trialtype)^ /*
*/ ^b1(*I:Treatment reduces blood pressure * Treatment increases blood pressure)^
. ^gen logor = (a*d)/(b*c)^
. ^gen selogor = sqrt( (1/a) + (1/b) + (1/c) + (1/d) )^
. ^metan7 logor selogor , eform effect(Odds ratio) ^
. ^metan7 percentlowerci upperci , wgt(n_positives) b1(Sensitivity)^ /*
*/ ^xlabel(0,20,40,60,80,100) nooverall^
. ^local ovratio=r(ES)^
. ^funnel , sample ysqrt xlabel(0.1,0.5,1,5,10) ylabel(0,500,1000)^ /*
*/ ^overall(`ovratio')^
. ^funnel or selogor, xlabel(0.1,0.5,1,5,10) ylabel(0,0.05,0.1) xlog^
. ^labbe tdeath tnodeath cdeath cnodeath, xlabel(0,0.25,0.5,0.75,1)^ /*
*/ ^ylabel(0,0.25,0.5,0.75,1) rr(0.91) rd(-0.021) ^
. ^metan7 n1 m1 sd1 n2 m2 sd2, nostandard^
. ^metan7 n1 m1 sd1 n2 m2 sd2, random xla(-2,-1,0,1,2) force saving(metagrph)^
Authors
-------
Michael J Bradburn, Jonathan J Deeks, Douglas G Altman.
Centre for Statistics in Medicine, University of Oxford,
Old Road Campus, Headington, Oxford OX3 7LF, UK.
email mike.bradburn@@cancer.org.uk
References
----------
Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis.
Statistics in Medicine 21:1539-1558
Also see
--------
STB: STB-44 sbe24
On-line: help for @meta@ (if installed), @metacum@ (if installed),
@metareg@ (if installed), @metabias@ (if installed),
@metatrim@ (if installed), @metainf@ (if installed),
@galbr@ (if installed)

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program metan_examples
version 9.0
`1'
end
program define metan_example_basic
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in wh ""
di ". metan tdeath tnodeath cdeath cnodeath, rd random"
di "> label(namevar=id, yearid=year) counts"
metan tdeath tnodeath cdeath cnodeath, rd random ///
label(namevar=id, yearid=year) counts
restore
end
program define metan_example_cols
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in wh ""
di ". metan tdeath tnodeath cdeath cnodeath,"
di "> sortby(year) lcols(id year country) rcols(population)"
di "> textsize(110) astext(60) double nostats nowt nohet notable"
metan tdeath tnodeath cdeath cnodeath, ///
sortby(year) lcols(id year country) rcols(population) ///
textsize(110) astext(60) double nostats nowt nohet notable
restore
end
program define metan_example_by
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". metan tsample tmean tsd csample cmean csd,"
di "> by(type_study) sgweight fixed second(random)"
di "> rfdist counts label(namevar = id)"
di "> favours(Treatment reduces blood pressure # Treatment increases blood pressure)"
metan tsample tmean tsd csample cmean csd, ///
by(type_study) sgweight fixed second(random) ///
rfdist counts label(namevar = id) ///
favours(Treatment reduces blood pressure # Treatment increases blood pressure)
restore
end
program metan_example_2param
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ""
di ". gen logor = ln( (tdeath*cnodeath)/(tnodeath*cdeath) ) )"
di ""
di ". gen selogor = sqrt( (1/tdeath) + (1/tnodeath) + (1/cdeath) + (1/cnodeath) )"
di ""
di ". metan logor selogor, eform xlabel(0.5, 1, 1.5, 2, 2.5)"
di "> force xtick(0.75, 1.25, 1.75, 2.25) effect(Odds ratio)"
gen logor = ln( (tdeath*cnodeath)/(tnodeath*cdeath) )
gen selogor = sqrt( (1/tdeath) + (1/tnodeath) + (1/cdeath) + (1/cnodeath) )
metan logor selogor, eform xlabel(0.5, 1, 1.5, 2, 2.5) ///
force xtick(0.75, 1.25, 1.75, 2.25) effect(Odds ratio)
restore
end
program define metan_example_diag
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". metan percent lowerci upperci, wgt(n_positives)"
di "> xlabel(0,10,20,30,40,50,60,70,80,90,100) force"
di "> null(50) label(namevar=id) nooverall notable"
di "> title(Sensitivity, position(6))"
di in gr ""
metan percent lowerci upperci, wgt(n_positives) ///
xlabel(0,10,20,30,40,50,60,70,80,90,100) force ///
null(50) label(namevar=id) nooverall notable ///
title(Sensitivity, position(6))
restore
end
program define metan_example_user
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". metan OR ORlci ORuci, wgt(bweight)"
di "> first(0.924 0.753 1.095 Bayesian)"
di "> firststats(param V=3.86, p=0.012)"
di "> label(namevar=id)"
di "> xlabel(0.25, 0.5, 1, 2, 4) force"
di "> null(1) aspect(1.2) scheme(economist)"
metan OR ORlci ORuci, wgt(bweight) ///
first(0.924 0.753 1.095 Bayesian) ///
firststats(param V=3.86, p=0.012) ///
label(namevar=id) ///
xlabel(0.25, 0.5, 1, 2, 4) force ///
null(1) aspect(1.2) scheme(economist)
restore
end
program define metan_example_custom
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ""
di `". gen counts = ". " + string(tdeath) + "/" + string(tdeath+tnodeath)"'
di `"> + ", " + string(cdeath) + "/" + string(cdeath+cnodeath)"'
di ""
di ". metan tdeath tnodeath cdeath cnodeath,"
di "> lcols(id year) notable"
di "> boxopt( mcolor(forest_green) msymbol(triangle) )"
di "> pointopt( msymbol(triangle) mcolor(gold) msize(tiny)"
di "> mlabel(counts) mlabsize(vsmall) mlabcolor(forest_green) mlabposition(1) )"
di "> ciopt( lcolor(sienna) lwidth(medium) )"
gen counts = ". " + string(tdeath) + "/" + string(tdeath+tnodeath) ///
+ ", " + string(cdeath) + "/" + string(cdeath+cnodeath)
metan tdeath tnodeath cdeath cnodeath, ///
lcols(id year) notable ///
boxopt( mcolor(forest_green) msymbol(triangle) ) ///
pointopt( msymbol(triangle) mcolor(gold) msize(tiny) ///
mlabel(counts) mlabsize(vsmall) mlabcolor(forest_green) mlabposition(1) ) ///
ciopt( lcolor(sienna) lwidth(medium) )
restore
end
program define funnel_example_immed
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". metan tdeath tnodeath cdeath cnodeath, nograph notable"
di ""
di ". local ovratio=r(ES)"
di ""
di ". funnel, sample ysqrt xlabel(0.1,0.5,1,5,10)"
di "> ylabel(0,500,1000) overall(`ovratio')"
metan tdeath tnodeath cdeath cnodeath, nograph notable
local ovratio=r(ES)
funnel, sample ysqrt xlabel(0.1,0.5,1,5,10) ///
ylabel(0,500,1000) overall(`ovratio')
restore
end
program define funnel_example_param
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". gen logor = ln( (tdeath*cnodeath)/(tnodeath*cdeath) ) )"
di ""
di ". gen selogor = sqrt( (1/tdeath) + (1/tnodeath) + (1/cdeath) + (1/cnodeath) )"
di ""
di ". funnel OR selogor, xlabel(0.1,0.5,1,5,10)"
di "> ylabel(0,1,2,3,4,5) xlog"
gen logor = ln( (tdeath*cnodeath)/(tnodeath*cdeath) )
gen selogor = sqrt( (1/tdeath) + (1/tnodeath) + (1/cdeath) + (1/cnodeath) )
funnel OR selogor, xlabel(0.1,0.5,1,5,10) ///
ylabel(0,1,2,3,4,5) xlog
restore
end
program define labbe_example
preserve
di ""
use http://fmwww.bc.edu/repec/bocode/m/metan_example_data.dta, clear
di in whi ""
di ". labbe tdeath tnodeath cdeath cnodeath,"
di "> xlabel(0,0.25,0.5,0.75,1) ylabel(0,0.25,0.5,0.75,1)"
di "> rr(1.029) rd(0.014) null"
labbe tdeath tnodeath cdeath cnodeath, ///
xlabel(0,0.25,0.5,0.75,1) ylabel(0,0.25,0.5,0.75,1) ///
rr(1.029) rd(0.014) null
restore
end

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*! 1.0.2 TJS 29 Feb 2004 Saves jackknived estimates
* 1.0.1 TJS 20 Jul 2001
* 1.0.0 TJS 22 May 2001
*! based on: metainf 3.0.0 AT Mar 2000 (STB-56: sbe26.1)
program define metaninf
version 6.0
syntax varlist(min=2 max=6 numeric) [if] [in] [, id(varname) noGRAPH /*
*/ LABEL(string) SAVe(string) t1(str) t2(str) Format(str) noTABLE `options' *]
tokenize `varlist'
* preserve
local est = "`1'"
if "`label'" != "" {
parse "`label'", parse("=,")
while "`1'" != "" {
cap confirm var `3'
if _rc != 0 {
di in re "Variable `3' not defined"
exit _rc
}
local `1' "`3'"
mac shift 4
}
}
tempvar code
qui {
if "`namevar'" != "" {
local lbnvl : value label `namevar'
if "`lbnvl'" != "" { quietly decode `namevar', gen(`code') }
else {
gen str10 `code' = ""
cap confirm string variable `namevar'
if _rc == 0 { replace `code' = `namevar' }
else if _rc == 7 { replace `code' = string(`namevar') }
}
}
else { gen str3 `code' = string(_n) }
if "`yearvar'" != "" {
local yearvar "`yearvar'"
cap confirm string variable `yearvar'
if _rc == 7 { local str "string" }
if "`namevar'" == "" { replace `code' = `str'(`yearvar') }
else { replace `code' = `code' + " (" + `str'(`yearvar') + ")" }
}
}
local id "`code'"
preserve
* Dealing with if and in options
if ("`if'" != "") { qui keep `if' }
if ("`in'" != "") { qui keep `in' }
if _N <= 1 { error 2001 }
* Overall estimates
tempvar so
qui gen `so' = _n
qui metan `varlist', nograph `options'
local ove = $S_1
local ll = $S_3
local ul = $S_4
sort `so'
* Meta-analysis estimate omiting one study each step
tempvar theta setheta ulth llth
qui sum `1', detail
local n = _result(1)
qui {
gen `theta' = .
gen `setheta' = .
gen `ulth' = .
gen `llth' = .
label var `theta' "Estimate"
label var `llth' "Lower CI Limit"
label var `ulth' "Upper CI Limit"
}
local i = 1
tempvar s
qui gen `s' = _n
while (`i' <= `n') {
qui {
metan `varlist' if `s' != `i', `options' nograph
sort `so'
replace `theta' = $S_1 in `i'
replace `llth' = $S_3 in `i'
replace `ulth' = $S_4 in `i'
}
local i = `i' + 1
}
* Maximum and minimum CI values
qui sum `llth', detail
local mnx = r(min)
qui sum `ulth', detail
local mxx = r(max)
* Labeling plot
if "`t2'" == "" { local t2 `""' }
if "`t1'" == "" { local t1 "Meta-analysis estimates, given named study is omitted" }
* Numeric format
if "`format'" == "" { local format "%5.2f" }
* Print option
if "`table'" != "notable" {
di
di in gr "------------------------------------------------------------------------------"
di in gr _col(2) "Study omitted" _col(20) "|" _col(24) "Estimate" _col(39) "[95% Conf. Interval]"
di in gr "-------------------+----------------------------------------------------------"
local i = 1
while `i' <= `n' {
if "`id'" == "" { local a = `s' in `i' }
else { local a = `id' in `i' }
local b = `theta' in `i'
local c = `llth' in `i'
local d = `ulth' in `i'
di _col(2) "`a'" _col(20) in gr "|" in ye _col(24) `b' _col(39) `c' _col(52) `d'
local i=`i'+1
}
di in gr "-------------------+----------------------------------------------------------"
di _col(2) "Combined" _col(20) in gr "|" in ye _col(24) `ove' _col(39) `ll' _col(52) `ul'
di in gr "------------------------------------------------------------------------------"
}
* Display plot
if "`graph'" != "nograph" {
mhplot `llth' `theta' `ulth', r sy(|o|) l("`id'") t1(`t1') t2(`t2') /*
*/ f(`format') xline(`ove',`ll',`ul') xlab(`mnx',`ove',`ll',`ul',`mxx') /*
*/ xti(`ove',`ll',`ul') xscale(`mnx',`mxx')
}
if "`save'" != "" {
local c = index("`save'",",")
if `c' != 0 {
local save = substr("`save'",1,`c'-1) + " " + substr("`save'",`c'+1, .)
}
local save1 : word 1 of `save'
local replace : word 2 of `save'
if "`replace'" == "replace" {
capture drop `save1'
}
capture confirm new var `save1'
if _rc {
local rc = _rc
di in re "`save1' exists. Use 'replace' option: save(save_var, replace)."
exit `rc'
}
qui {
gen `save1' = `theta'
sort `id'
tempfile saved
save `saved'
restore
sort `id'
merge `id' using `saved', keep(`save1') update replace nokeep
drop if _merge==2
drop _merge
}
label var `save1' "jackknifed `est' (metaninf)"
end

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/*
*! metaninf dialog version 1.0.2, 29 Feb 2004, T. J. Steichen, steichen@triad.rr.com
*! for metaninf version 1.0.2, 29 Feb 2004, T. J. Steichen, steichen@triad.rr.com
Influence of a single study in meta-analysis estimation
-------------------------------------------------------
Syntax: metaninf varlist [if exp] [in range] [, fixed fixedi random randomi
rr or rd peto cornfield hedges cohen glass nostandard
label(namevar=namevar[, yearvar=yearvar])
ilevel(#) olevel(#) notable nograph save(varname [, replace]) ]
Install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "metan&inf (Metan-based Influence Analysis)" "db metaninf"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE _ht240
INCLUDE header
HELP hlp1, view("help metaninf")
RESET res1, label("Reset")
DIALOG main, label("metaninf 1.0.2 - Metan-based Influence Analysis") tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 330 _ht1h, ///
label("Type of Data:")
RADIO r_binary 15 25 100 ., ///
label("Binary Count") first ///
onclickon(script main_binary_on) ///
onclickoff(script main_binary_off)
RADIO r_continuous 125 25 100 ., ///
label("Continuous") middle ///
onclickon(script main_continuous_on) ///
onclickoff(script main_continuous_off)
RADIO r_effect 235 25 100 ., ///
label("Effect Size") last ///
onclickon(script main_effect_on) ///
onclickoff(script main_effect_off)
TEXT tx_binary 10 50 _iwd ., ///
label("Vars for Counts a, b, c, d, in that order")
VARLIST vl_binary @ _ss @ ., ///
label("Vars for a, b, c, d")
TEXT tx_contin_exp @ 50 _iwd ., ///
label("Vars for Experimental Group: n, mean, sd, in that order")
VARLIST vl_contin_exp @ _ss @ ., ///
label("Experimental Group: n, mean, sd")
TEXT tx_contin_ctl @ _ss _iwd ., ///
label("Vars for Control Group: n, mean, sd, in that order")
VARLIST vl_contin_ctl @ _ss @ ., ///
label("Control Group: n, mean, sd")
TEXT tx_effect @ 50 _iwd ., ///
label("Vars for Effect Sizes: theta, se(theta), in that order")
VARLIST vl_effect @ _ss @ ., ///
label("Effect Sizes: theta, se(theta)")
GROUPBOX gb_labels 10 130 330 _ht3h, ///
label("Labels for Data:")
CHECKBOX cb_name 20 150 70 ., ///
label("Name:") ///
onclickon(main.vn_name.enable) ///
onclickoff(main.vn_name.disable) ///
option("id")
VARNAME vn_name 100 150 230 ., ///
label("Name Variable")
CHECKBOX cb_year 20 170 70 ., ///
label("Year:") ///
onclickon(main.vn_year.enable) ///
onclickoff(main.vn_year.disable) ///
option("id")
VARNAME vn_year 100 170 230 ., ///
label("Year Variable")
CHECKBOX cb_save_jk 10 200 330 ., ///
label("Save jack-knived estimates: varname [, replace]") ///
onclickon(main.vn_save_jk.enable) ///
onclickoff(main.vn_save_jk.disable)
VARNAME vn_save_jk 10 220 330 ., ///
label("Save estimates Variable") ///
option("save")
END
DIALOG metan_b, tabtitle("Binary Opts")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht9h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Mantel Haenzel") first ///
option("fixed")
RADIO r_fixedi @ _ss @ ., ///
label("Fixed, Inverse Variance") middle ///
option("fixedi")
RADIO r_random @ _ss @ ., ///
label("Random, Mantel Haenzel") middle ///
option("random")
RADIO r_randomi @ _ss @ ., ///
label("Random, Inverse Variance") middle ///
option("randomi")
RADIO r_peto @ _ss @ ., ///
label("Peto") last ///
option("peto")
GROUPBOX gb_stat 220 10 120 _ht5h, ///
label("Statistic")
RADIO r_rr 230 30 80 ., ///
label("RR") first ///
option("rr")
RADIO r_or @ _ss @ ., ///
label("OR") middle ///
onclickon(script metan_b_or_on) ///
onclickoff(script metan_b_or_off) ///
option("or")
RADIO r_rd @ _ss @ ., ///
label("RD") last ///
option("rd")
CHECKBOX cb_cornfield 10 140 200 ., ///
label("Use Cornfield CI's") ///
option("cornfield")
CHECKBOX cb_chi2 10 160 200 ., ///
label("Use Chi-2 Statistic") ///
option("chi2")
CHECKBOX cb_breslow 10 180 200 ., ///
label("Use Breslow-Day test") ///
option("breslow")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
END
DIALOG metan_c, tabtitle("Contin. Opts")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random, Inverse Variance") last ///
option("random")
GROUPBOX gb_stat 220 10 120 _ht7h, ///
label("Statistic")
RADIO r_cohen 230 30 100 ., ///
label("Cohen") first ///
option("cohen")
RADIO r_hedges @ _ss @ ., ///
label("Hedges") middle ///
option("hedges")
RADIO r_glass @ _ss @ ., ///
label("Glass") middle ///
option("glass")
RADIO r_nostandard @ _ss @ ., ///
label("noStandard") last ///
option("nostandard")
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
END
DIALOG metan_e, tabtitle("Effect Opts")
BEGIN
GROUPBOX gb_pool 0 10 210 _ht3h, ///
label("Pooling Model")
RADIO r_fixed 10 30 190 ., ///
label("Fixed, Inverse Variance") first ///
option("fixed")
RADIO r_random @ _ss @ ., ///
label("Random, Inverse Variance") last ///
option("random")
/*
GROUPBOX gb_stat 220 10 120 _ht5h, ///
label("Statistic")
RADIO r_rr 230 30 80 ., ///
label("RR") first ///
option("rr")
RADIO r_or @ _ss @ ., ///
label("OR") middle ///
option("or")
RADIO r_rd @ _ss @ ., ///
label("RD") last ///
option("rd")
*/
CHECKBOX cb_notable 230 140 200 ., ///
label("noTable") ///
option("notable")
CHECKBOX cb_nograph 230 160 200 ., ///
label("noGraph") ///
option("nograph")
END
INCLUDE ifin
SCRIPT main_binary_on
BEGIN
main.tx_binary.show
main.vl_binary.show
main.tx_binary.enable
main.vl_binary.enable
main.tx_effect.disable
main.vl_effect.disable
main.tx_effect.hide
main.vl_effect.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_binary_off
BEGIN
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
END
SCRIPT main_continuous_on
BEGIN
main.tx_contin_exp.show
main.vl_contin_exp.show
main.tx_contin_exp.enable
main.vl_contin_exp.enable
main.tx_contin_ctl.show
main.vl_contin_ctl.show
main.tx_contin_ctl.enable
main.vl_contin_ctl.enable
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_effect.disable
main.vl_effect.disable
main.tx_effect.hide
main.vl_effect.hide
END
SCRIPT main_continuous_off
BEGIN
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_on
BEGIN
main.tx_effect.show
main.vl_effect.show
main.tx_effect.enable
main.vl_effect.enable
main.tx_binary.disable
main.vl_binary.disable
main.tx_binary.hide
main.vl_binary.hide
main.tx_contin_exp.disable
main.vl_contin_exp.disable
main.tx_contin_exp.hide
main.vl_contin_exp.hide
main.tx_contin_ctl.disable
main.vl_contin_ctl.disable
main.tx_contin_ctl.hide
main.vl_contin_ctl.hide
END
SCRIPT main_effect_off
BEGIN
main.tx_effect.disable
main.vl_effect.disable
main.tx_effect.hide
main.vl_effect.hide
END
SCRIPT metan_b_or_on
BEGIN
metan_b.r_peto.enable
metan_b.cb_cornfield.enable
metan_b.cb_chi2.enable
metan_b.cb_breslow.enable
END
SCRIPT metan_b_or_off
BEGIN
metan_b.r_peto.disable
metan_b.cb_cornfield.disable
metan_b.cb_chi2.disable
metan_b.cb_breslow.disable
END
PROGRAM command
BEGIN
put "metaninf "
if main.r_binary {
varlist main.vl_binary
}
if main.r_continuous {
varlist main.vl_contin_exp main.vl_contin_ctl
}
if main.r_effect {
varlist main.vl_effect
}
INCLUDE _ifin_pr
beginoptions
if main.cb_name | main.cb_year {
put "label("
if main.cb_name {
put "namevar=" main.vn_name
}
if main.cb_name & main.cb_year {
put ", "
}
if main.cb_year {
put "yearvar=" main.vn_year
}
put ") "
}
if main.r_binary {
option radio(metan_b r_fixed r_fixedi r_random r_randomi)
option radio(metan_b r_rr r_or r_rd)
option metan_b.cb_cornfield
option metan_b.cb_chi2
option metan_b.cb_breslow
option metan_b.cb_nograph
option metan_b.cb_notable
}
if main.r_continuous {
option radio(metan_c r_fixed r_random)
option radio(metan_c r_cohen r_hedges r_glass r_nostandard)
option metan_c.cb_nograph
option metan_c.cb_notable
}
if main.r_effect {
option radio(metan_e r_fixed r_random)
/* option radio(metan_e r_rr r_or r_rd) */
option metan_e.cb_nograph
option metan_e.cb_notable
}
optionarg main.vn_save_jk
endoptions
END

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.-
help for ^metaninf^ version 1.0.2
.-
Influence of a single study in meta-analysis estimation
-------------------------------------------------------
^metaninf^ varlist [^if^ exp] [^in^ range] [, ^fixed fixedi random randomi^
^rr or rd peto cornfield hedges cohen glass nosta^ndard
^label(^label_vars^) il^evel^(^#^) ol^evel^(^#^) notable nograph^
^sav^e^(^varname [^, replace^]^)^ ]
Description
-----------
^metaninf^ investigates the influence of each individual study on the overall
meta-analysis summary estimate. The command presents a table and a graph of
the results of an influence analysis in which the meta-analysis is reestimated
omitting each study in turn.
The table numerically provides the results, with the rows of the table being
the meta-analysis of all studies except the "omitted" study named in that row,
and the usual, full meta-analysis (omitting none of the studies) being given
as the "combined" results at the bottom of the table.
The graph visually provides the same results in a plot, naming the omitted
study on the left margin and presenting the resulting "omitted" meta-analytic
summary statistics as a horizontal confidence interval. The full, "combined"
results are shown as the solid vertical lines.
No formal test of influence is given; rather, the program provides and displays
results to which some general guidelines can be applied to assess influence.
One such guideline is that an individual study is suspected of excessive
influence if the point estimate of its "omitted" analysis lies outside the
confidence interval of the "combined" analysis. Another quideline is that a
study is excessively influential if its "omitted" meta-analytic estimate
differs in significance relative to the "combined" analysis. Neither of these
quidelines provides definitive proof that such a study should, or should not,
be removed from the analysis; they merely provide a suggestion that some
attention be paid to potential reasons for its influence.
^metaninf^ uses program ^metan^ as its meta-analysis engine and its options are
a direct subset of ^metan^'s. It is expected that users will merely edit a ^metan^
command line by adding the "inf" suffix to the program name to run ^metaninf^.
Like ^metan^, ^metaninf^ requires either four or six variables to be declared.
When four variables are specified, analysis of binary data is performed.
When six variables are specified, the data are assumed continuous.
Binary data is presented as the 4 cell counts (a b c d) of a 2x2 table where
a and b are the number of event & no-event subjects in the intervention group
and c and d are similar numbers for the control group.
. ^metaninf a b c d^
Continuous data is presented as 6 values: the n, mean and sd for the treatment
(or intervention) group followed by similar values for the control group.
. ^metaninf nt mt sdt nc mc sdc^
Options
-------
Scaling and pooling options
---------------------------
Options for binary data
^rr^ pools risk ratios (the default).
^or^ pools odds ratios.
^rd^ pools risk differences.
^fixed^ specifies a fixed effect model using the method of Mantel & Haenszel
(the default).
^fixedi^ specifies a fixed effect model using the inverse variance method.
^peto^ specifies that Peto's assumption free method is used to pool odds
ratios.
^random^ specifies a random effects model using the method of DerSimonian &
Laird, with the estimate of heterogeneity being taken from the
Mantel-Haenszel model.
^randomi^ specifies a random effects model using the method of DerSimonian &
Laird, with the estimate of heterogeneity being taken from the inverse
variance fixed effect model.
^cornfield^ computes confidence intervals for odds ratios by the method of
Cornfield, rather than the (default) Woolf method.
Options for continuous data
^cohen^ pools standardized mean differences by the method of Cohen
(the default).
^hedges^ pools standardized mean differences by the method of Hedges.
^glass^ pools standardized mean differences by the method of Glass.
^nostandard^ pools unstandardized mean differences.
^fixed^ specifies a fixed effect model using the inverse variance method
(the default).
^random^ specifies a random effects model using the DerSimonian & Laird
method.
General output options
----------------------
^label(^[^namevar^=namevar] [^, yearvar^=yearvar]^)^
labels the data by its name, year or both. However, neither option
is required. For the table display the overall length of the label is
restricted to 16 characters.
^ilevel()^ specifies the significance level (eg 90,95,99) for the
individual trial confidence intervals.
^olevel()^ specifies the significance level (eg 90,95,99) for the
overall (pooled) confidence intervals.
^ilevel^ and ^olevel^ need not be the same, and by default are equal
to the significance level specified using the ^set level^ command.
^notable^ prevents display of the table of results.
^nograph^ prevents display of the graph.
^save(^varname [^, replace^]^)^ saves the jackknifed estimates.
Note
----
To run ^metaninf^, the ^metan^ command [STB-44: sbe24] must be installed.
Acknowledgements
----------------
^metaninf^ is a direct rip-off of ^metainf^ (by Aurelio Tobias; see STB-47: sbe26;
STB-56: sbe26.1). It blatently uses ^metan^ (by Michael J Bradburn, Jonathan J
Deeks and Douglas G Altman; see STB-44: sbe24) as its meta-analysis calculation
engine and ^mhplot^, a slight variation of ^hplot^ (by Nicolas Cox), as its
graphic engine. The current author is indebted to the above authors for all
their hard work, thus allowing this program to be written with no apparent
personal intellectual input. Thanks guys!
Examples
--------
. ^metaninf a b c d, fixedi or label(namevar=studyid, yearvar=year)^
. ^metaninf nt mt sdt nc mc sdc, cohen notable^
Author
------
Thomas J Steichen, steichen@triad.rr.com
Also see
--------
On-line: help for @metan@, and if installed: @metainf@, @meta@, @metareg@, @metacum@,
@metabias@, @metatrim@, @metap@, @galbr@, @funnel@, @labbe@, and @hplot@.

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*!version 1.0 MJB 9 May 2003
cap program drop metannt
program define metannt , rclass
version 7.0
syntax [, measure(string) size(string) confint(string) baseline(string)]
*If measure size & confint left unspecified, assume that these are as stored
*from previous metan/meta-analysis in r(ES) etc
if "`measure'`size'`confint'"=="" {
*Get effect size, conf intervals and type of summary measure from metan
local measure = r(measure)
local size = r(ES)
local conf_l= r(ci_low)
local conf_u= r(ci_upp)
}
else {
*otherwise, user is assumed to have specified them: conf interval needs parsing (and is optional)
local measure=upper("`measure'")
if "`confint'"!="" {
tokenize "`confint'", parse(",")
local conf_l=`1'
local conf_u=`3'
}
}
cap {
if ("`measure'"=="SMD" | "`measure'"=="WMD" | "`measure'"=="ES")
di in re "metannt valid only after binary data meta-analysis"
exit
}
cap {
assert ("`measure'"=="OR" | "`measure'"=="RR" | "`measure'"=="RD")
assert `size'>-1
assert `size'>0 if ("`measure'"=="OR" |"`measure'"=="RR")
assert `size'<1 if "`measure'"=="RD"
assert `size'!=.
}
if _rc!=0 {
di in re "Specify a valid measure AND effect size using measure( ) and size( ) options,"
di in re " or use last estimates from metan"
exit
}
if "`conf_l'"!="" {
cap {
assert `conf_l'>=-1
assert `conf_l'>0 if "`measure'"!="RD"
assert `conf_u'<=1 if "`measure'"=="RD"
assert `size'>`conf_l'
assert `size'<`conf_u'
}
if _rc!=0 {
di in re "Invalid confidence interval"
exit
}
}
if "`baseline'"=="" {
local baseline =r(cger)
local note2 "**" /* to reinforce estimation of CGER from data */
}
if ( (`size'>0 & "`measure'"=="RD") | (`size'>1 & "`measure'"!="RD") ) {
local directn "excess"
local nntstub "H"
}
else {
local directn "avoided"
local nntstub "B"
}
if "`conf_l'"!="" {
local ci "(CI)"
local cont "_cont"
}
di _n in gr " Control group | Treatment group |" _col(43) "| No. of `directn' "
di in gr " event rate | event rate *" _col(34) "| NNT`nntstub'* | events per 1000 `ci'*"
di in gr _dup(15) "-" "+" _dup(17) "-" "+" _dup(8) "-" "+" _dup(26) "-"
*error check
local flag=0
parse "`baseline'", parse(",")
while "`1'"!="" {
local cger=`1'
*calculate tger then NNT / events from d-a
if "`measure'"=="RD" {
local tger=`cger'+`size'
if "`ci'"!="" {
local tger_ll = `cger'+`conf_l'
local tger_ul = `cger'+`conf_u'
}
}
if "`measure'"=="RR" {
local tger=(`cger'*`size')
if "`ci'"!="" {
local tger_ll =(`cger'*`conf_l')
local tger_ul =(`cger'*`conf_u')
}
}
if "`measure'"=="OR" {
local tger=(`size'*`cger')/(1-`cger'+`cger'*`size')
if "`ci'"!="" {
local tger_ll =(`conf_l'*`cger')/(1-`cger'+`cger'*`conf_l')
local tger_ul =(`conf_u'*`cger')/(1-`cger'+`cger'*`conf_u')
}
}
if (`tger'>=0 & `tger'<=1 & `cger'>=0 & `cger'<=1) {
local nnt = abs(1/(`cger'-`tger'))
local np1000=abs(1000*(`cger'-`tger'))
if "`ci'"!="" {
*direction is important for CI
if `cger'>`tger' {
*events avoided: CI goes from (cg-max_tg to cg-min_tg)
local np1k_ll =(1000*(`cger'-`tger_ul'))
local np1k_ul =(1000*(`cger'-`tger_ll'))
}
else {
*events excess: CI goes from (tg_min-cg to tg_max-cg)
local np1k_ll =(1000*(`tger_ll'-`cger'))
local np1k_ul =(1000*(`tger_ul'-`cger'))
}
}
}
else {
local flag=10
local nnt =.
local np1000 =.
}
di in ye _col(5) %6.3f `cger' " `note2'" _col(23) %6.3f `tger' _col(35) %6.1f `nnt' /*
*/ _col(48) %5.1f `np1000' `cont'
if "`ci'"!="" {
di in ye " (" %5.1f `np1k_ll' " , " %5.1f `np1k_ul' ")
local tger_ll = `cger'+`conf_u'
}
mac shift 2
}
if `flag'>1 { di in bl "Note some baseline/event rate combinations are invalid"}
di _n in bl "* based on `measure'=" %5.3f `size' " applied to the control group event rate(s)"
if "`note2'"=="**" {
di in bl "** based on an assumed average control group event rate of `cger'"
return scalar cger= `cger'
return scalar tger= `tger'
return scalar nnt= `nnt'
return scalar ep1000= `np1000'
end

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/*
*! metannt dialog version 1.0.1, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metannt version 1.0, 9 May 2003, M. J. Bradburn, mike.bradburn@cancer.org.uk
Metan-based estimates of absolute benefit or risk
-------------------------------------------------
Syntax: metannt , baseline(#[,#...#]) [ measure(or|rr|rd) size(#) confint(#,#) ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Metan-based NNT (metann&t)" "db metannt"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metannt")
RESET res1, label("Reset")
DIALOG main, label("metannt 1.0 - Metan-based NNT") tabtitle("Main")
BEGIN
TEXT tx_base 10 10 150 ., ///
label("Baseline(s): # , # , ...")
EDIT ed_base 10 30 330 ., ///
label("Baseline value(s)") ///
option("baseline")
CHECKBOX cb_effect 10 65 310 ., ///
label("Enter Values (default: use metan values)") ///
onclickon(script main_opts_on) ///
onclickoff(script main_opts_off)
FRAME fr_enter 10 90 330 100
GROUPBOX gb_stat 20 95 70 _ht5h, ///
label("Measure")
RADIO r_rr 25 115 50 ., ///
label("RR") first ///
onclickon(script main_rr_on) ///
option("measure(rr)")
RADIO r_or @ _ss @ ., ///
label("OR") middle ///
onclickon(script main_or_on) ///
option("measure(or)")
RADIO r_rd @ _ss @ ., ///
label("RD") last ///
onclickon(script main_rd_on) ///
option("measure(rd)")
TEXT tx_size 180 115 150 ., ///
label("Effect Size: #")
EDIT ed_size 100 @ 40 ., ///
label("Effect Size") ///
numonly ///
option("size")
TEXT tx_ci 180 140 150 ., ///
label("Effect Size CI: # , #")
EDIT ed_ci 100 @ 70 ., ///
label("Effect Size CI") ///
option("confint")
END
SCRIPT main_opts_on
BEGIN
main.gb_stat.enable
main.r_rr.enable
main.r_or.enable
main.r_rd.enable
main.tx_ci.enable
main.ed_ci.enable
main.tx_size.enable
main.ed_size.enable
END
SCRIPT main_opts_off
BEGIN
main.gb_stat.disable
main.r_rr.disable
main.r_or.disable
main.r_rd.disable
main.tx_ci.disable
main.ed_ci.disable
main.tx_size.disable
main.ed_size.disable
END
SCRIPT main_rr_on
BEGIN
main.tx_size.setlabel "Effect Size: # > 0"
main.tx_ci.setlabel "Effect Size CI: 0 < # , #"
END
SCRIPT main_or_on
BEGIN
main.tx_size.setlabel "Effect Size: # > 0"
main.tx_ci.setlabel "Effect Size CI: 0 < # , #"
END
SCRIPT main_rd_on
BEGIN
main.tx_size.setlabel "Effect Size: -1 < # < 1"
main.tx_ci.setlabel "Effect Size CI: -1 < # < 1"
END
PROGRAM command
BEGIN
require main.ed_base
require main.ed_size
put "metannt "
beginoptions
option radio(main r_rr r_or r_rd)
optionarg main.ed_base
optionarg main.ed_size
optionarg main.ed_ci
endoptions
END

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.-
help for ^metannt^
.-
Estimates of absolute benefit or risk from meta-analysis
--------------------------------------------------------
^metannt , measure(^or|rr|rd^) size(^#^) confint(^#,#^) baseline(^#[,#...#]^)^
Description
-----------
This program is intended to aid interpretation of meta-analyses of binary
data by presenting the effect sizes in absolute terms. Both the number
needed to treat (NNT) and the number of events avoided (or added) per
1000 are presented.
The ^Number Needed to Treat (NNT)^ is the number of individuals required
to experience the intervention in order to expect there to be one
additional event to be observed. Assuming the event is undesireable, this
is termed the "number needed to treat to benefit" (NNTB). If the
intervention arm experiences more events, this is commonly refered to as
the "number needed to treat to harm" (NNTH).
^metannt^ calculates this by deriving an effect size (e.g. a risk ratio),
applying it to a population with a given event prevalance, and from this
deriving a projected event rate if the population were to receive the
intervention. The NNT is equal to
1/(control group event rate - treatment group event rate).
The ^number of avoided or excess events (respectively) per 1000 population^
is the difference between the two event rates multiplied by 1000. The
intervention group event rate is calculated in the same manner as with
the NNT. Optionally a confidence interval is also presented, using the
confidence limits of the effect size applied to the control group event
rate.
Options for ^metannt^
-------------------
^baseline()^ specifies the baseline (ie. control group) event rates, from
which the NNT and number of events avoided are computed from. More than
one baseline event rate may be specified.
^measure(^rr|or|rd^)^ specifies whether the estimated effect size pooled is
a risk ratio, odds ratio or (absolute) risk difference.
^size(^#^)^ denotes the size of the effect
^confint(^#,#^)^ denotes the confidence interval around the effect size.
By default the saved results from the r() macros within @metan@ are used to
derive the control group event rate, the effect measure, the effect size and
its confidence interval. However, unless a recent version of metan (1.71
onwards) was used immediately beforehand, the first three options must be
specified. The confidence interval is optional.
Examples
--------
. ^metannt , measure(rr) size(0.2) baseline(0.1,0.2,0.3)^
. ^metan tdeath tnodeath cdeath cnodeath, or^
. ^metannt , baseline(0.15)^
Authors
-------
Michael J Bradburn, Jonathan J Deeks, Douglas G Altman.
Centre for Statistics in Medicine, Institute of Health Sciences,
Old Road, Headington, Oxford OX3 7LF, UK.
email mike.bradburn@@cancer.org.uk
Also see
--------
On-line: help for @metan@ (if installed), @meta@ (if installed)

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/*
*! metap dialog version 1.0.0, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metap version 2.0.0, Mar 2000, Aurelio Tobias, atobias@isciii.es
Meta-analysis of p-values
-------------------------
Syntax: metap pvar [if exp] [in range] [ , method(#) ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-analysis of p-values (meta&p)" "db metap"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metap")
RESET res1, label("Reset")
DIALOG main, label("metap 2.0.0 - Meta-analysis of p-values") tabtitle("Main")
BEGIN
TEXT tx_p 10 10 145 ., ///
label("Variable for p-values:")
VARNAME vn_p 150 @ 190 ., ///
label("Variable for p-values")
GROUPBOX gb_method 10 40 330 _ht5h, ///
label("Method:")
RADIO r_f 15 55 200 ., ///
label("Fisher's") first ///
option(NONE)
RADIO r_ea 15 75 200 ., ///
label("Edgington's additive") middle ///
option("method(ea)")
RADIO r_en 15 95 200 ., ///
label("Edgington's normal curve") last ///
option("method(en)")
END
INCLUDE ifin
PROGRAM command
BEGIN
put "metap "
varlist main.vn_p
INCLUDE _ifin_pr
beginoptions
option radio(main r_f r_ea r_en)
endoptions
END

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*! v2.6.1 Roger Harbord 4 Nov 2008
program define metareg, eclass byable(recall)
version 7
if replay() {
if "`e(cmd)'" !="metareg" {
error 301 /* last estimates not found */
}
if _by() {
error 190 /* request may not be combined with by */
}
Display `0'
exit
}
syntax varlist(min=1 numeric) [if] [in] , [ /*
*/ wsse(varname numeric) /*
*/ EForm /*
*/ NOCONStant /*
*/ MM /*
*/ REML /*
*/ EB /*
*/ Knapphartung /*
*/ z /* standard normal w/o K-H modification to variance
*/ Level(passthru) /*
*/ PERMute(string) /*
*/ Graph /* produce a bubble plot
*/ RAndomsize /* marker size according to random-effects weights
*/ TAU2test /* tests for tau2=0 residual het test
*/ LOg /* maximize_option
*/ TOLerance(real 1e-6) /* maximize_option also used by eb method
*/ ITERate(integer 100) /* maximize_option - default 16000 is too large!
*/ wsvar(varname numeric)/* undoc'd, for compat with v1 ( wsvar = wsse^2 )
*/ bsest(string) /* undoc'd, for compat with v1
*/ tdist /* undoc'd, use t-distib w/o K-H variance mod'n
*/ ORIGinal /* undoc'd, use version 1 (metareg_orig)
*/ LRtau2 /* undoc'd, for compat with v.2.2 on SSC
*/ NOITer /* passed through to metareg_orig
*/ NOTAUComp /* no comparison of tau2 with constant-only model;
for use by permute() option
*/ * /* other `options', parsed by -mlopts-
*/ ]
marksample touse
tokenize `varlist'
local y `1' /* study estimate */
qui count if `touse'
local nobs = r(N)
if r(N) == 0 {
error 2000 /* no observations */
}
if r(N) <= `: word count `*'' {
error 2001 /* insufficient observations */
}
macro shift 1
_rmcoll `*' if `touse', `noconstant'
local xvars `r(varlist)'
mlopts mlopts, `options' iterate(`iterate') /* Parse maximize_options */
if "`wsse'"=="" & "`wsvar'"=="" {
di as error "wsse() option required"
exit 100
}
if "`wsse'"!="" & "`wsvar'"!="" {
di as error "options wsse() and wsvar() cannot both be specified"
exit 198
}
if "`wsse'"!="" {
capture assert `wsse' > 0 if `touse'
if _rc {
di as error "zero or negative wsse() not allowed"
exit 499
}
tempvar wsvar
qui gen `wsvar' = `wsse'^2 if `touse'
}
if "`wsvar'"!="" {
capture assert `wsvar' > 0 if `touse'
if _rc {
di as error "zero or negative wsvar() not allowed"
exit 499
}
}
if "`noconstant'"!="" {
/* local nvar : word count `varlist' */
if `: word count `varlist'' == 1 {
di as error "independent variables required with " /*
*/ "noconstant option"
exit 102
}
}
local wc : word count "`mm' `reml' `eb'"
if `wc' > 1 {
di as error "Only one of options mm, reml and eb can be specified"
exit 198
}
local wc : word count "`mm' `reml' `eb' `bsest'"
if `wc' > 1 {
di as error "Option bsest() cannot be used with options mm, reml or eb"
exit 198
}
if "`bsest'"!="reml" & "`bsest'"!="ml" & "`bsest'"!="eb" /*
*/ & "`bsest'"!="mm" & "`bsest'"!="" {
di as error "Between study variance estimation method invalid:"
di as error "should be either reml, ml, eb or mm"
exit 198
}
if "`bsest'" == "" {
if "`mm'" != "" | "`permute'" != "" { /* MM is default with permute */
local bsest "mm"
}
else if "`eb'" != "" {
local bsest "eb"
}
else { /* REML is default otherwise */
local bsest "reml"
}
}
if "`bsest'" =="ml" {
di as text /*
*/ "Option bsest(ml) not supported by version 2 of metareg."
di as text "Calling version 1.06 (sbe23):" _n(1)
metareg_orig `0'
exit
}
if "`knapphartung'" !="" & "`z'" != "" {
di as error "Options knapphartung and z cannot both be specified"
exit 198
}
if "`tdist'" != "" & ( "`z'" != "" | "`knapphartung'" !="" ) {
di as error "Option t cannot be specified with options knapphartung or z"
exit 198
}
if "`original'" =="original" {
di as text "Calling metareg version 1.06 (sbe23):" _n(1)
metareg_orig `0'
exit
}
if "`log'"=="" {
local log "nolog"
}
if "`lrtau2'" != "" {
local tau2test tau2test
}
if "`permute'" != "" {
if "`xvars'" == "" {
di as error "Option permute() requires at least one covariate"
exit 198
}
Permute, y(`y') xvars(`xvars') wsvar(`wsvar') permute(`permute') ///
nobs(`nobs') `noconstant' ///
bsest(`bsest') `knapphartung' `z' ///
level(`level') touse(`touse')
exit
}
/*** begin main code ***/
tempvar w hat v resid
tempname remll_c remll sumw tau2 tau2mm b V Q df_Q tau2_0
quietly {
/** (get starting values using) method-of-moments **/
gen `w'=1/`wsvar' if `touse'
summ `w' if `touse', meanonly
scalar `sumw'=r(sum)
/* use regress not vwls so can predict hat */
regress `y' `xvars' [iw=`w'] if `touse', `noconstant'
predict `hat' if `touse', hat
/* H_ii where H = X(X'WX)^{-1}X', W=diag(w) */
replace `hat'=`hat'*`w'^2 if `touse' /* Tr( WX(X'WX)^{-1}X'W ) */
/* iweights mess up residual df so compute it : */
scalar `df_Q' = `nobs' - e(df_m) - ("`noconstant'" == "")
scalar `Q' = e(rss)
summ `hat' if `touse', meanonly
scalar `tau2' = (`Q' - `df_Q') / ( `sumw' - r(sum))
/* truncate at zero */
scalar `tau2' = max(`tau2', 0)
/* Fit constant-only model to compare tau2
NB can't compare different fixed-effect models using REML log-L */
if "`noconstant'" == "" & "`notaucomp'"=="" & "`xvars'" != "" {
metareg `y' if `touse', wsvar(`wsvar') bsest(`bsest') notaucomp
scalar `tau2_0' = e(tau2)
}
} /* end quietly */
if "`bsest'" == "reml" { /* *** REML start *** */
global REML_x `xvars' /* betas not estimated by ml so pass X as global*/
global REML_nocons `noconstant' /* have to pass as global too */
/* fit comparison fixed-effect (tau2=0) model*/
ml model d0 metareg_ll (`y' `wsvar' = ) /*
*/ if `touse', maximize init(_cons=0) /*
*/ search(off) iterate(0) nowarning nolog
scalar `remll_c' = e(ll) /* log-L, comparison model */
/* estimate tau2 using ml */
local tau2mm = max(`tau2', 1e-4) /* init doesn't work with scalar */
ml model d0 metareg_ll (`y' `wsvar' = ) if `touse',/*
*/ maximize init(_cons=`tau2mm') search(off) nopreserve noscvars /*
*/ `log' tolerance(`tolerance') `mlopts'
if "`e(converged)'" != "1" {
di as error "WARNING: REML estimation of tau2 failed to converge."
di as error "Estimates may be wrong."
di as error "Try adding mm option to use method-of-moments".
}
macro drop REML_x
macro drop REML_nocons
scalar `tau2' = max( _b[_cons], 0)
if `tau2' > 0 {
scalar `remll' = e(ll)
}
else {
scalar `remll' = `remll_c'
}
} /* REML end */
if "`bsest'" == "eb" { /* *** EB start *** */
local oldtau2=-1
local j=0
while abs((`tau2'-`oldtau2')/(`oldtau2'+1e-8)) >= `tolerance' {
local j = `j'+1
tempvar wt ypred wtsq numi
local oldtau2=`tau2'
qui {
gen `wt'=(`wsvar'+`tau2')^-1 if `touse'
regress `y' `xvars' [aw=`wt'] if `touse'
predict `ypred' if `touse'
gen `wtsq'=`wt'^2 if `touse'
gen `numi'=(`wt')*(((e(N)/e(df_r))*((`y'-`ypred')^2))-`wsvar') if `touse'
summ `numi' if `touse', meanonly
local num=r(sum)
summ `wt', meanonly
local tau2=`num'/r(sum)
}
if "`log'" == "log" {
di as txt "Iteration " `j' ": tau^2 = " as res `tau2'
}
}
local tau2 = max(`tau2',0)
} /* EB end*/
/** estimate final model by weighted LS **/
quietly {
gen `v' = `wsvar' + `tau2'
regress `y' `xvars' [iw=1/`v'] if `touse', mse1 `noconstant'
matrix `b' = e(b)
matrix `V' = e(V)
local df_m = e(df_m)
/** knapp and hartung variance adjustment **/
if "`z'" == "" & "`tdist'" == "" {
tempname vkh
predict `resid' if `touse', resid
replace `resid' = `resid'*`resid' / `v' if `touse'
summarize `resid' if `touse', meanonly
local qkh = r(sum) / `df_Q'
matrix `V' = `V' * max(1, `qkh')
}
} /* end quietly */
estimates post `b' `V', depname(`y') obs(`nobs') esample(`touse')
estimates scalar tau2 = `tau2'
estimates scalar df_m = `df_m'
estimates scalar Q = `Q'
estimates scalar df_Q = `df_Q'
estimates scalar I2 = max( ( `Q' - `df_Q' ) / `Q', 0 )
if "`z'" == "" { /* post df_r so get t & F tests not z and chi2 */
estimates scalar df_r = `df_Q'
}
if "`tdist'" =="" & "`z'" == "" {
estimates scalar q_KH = `qkh'
}
if "`wsse'" == "" {
estimates local wsvar "`wsvar'"
}
else {
estimates local wsse "`wsse'"
}
if "`bsest'" == "mm" {
estimates local method "Method of moments"
}
if "`bsest'" == "eb" {
estimates local method "Empirical Bayes"
}
if "`bsest'" == "reml" {
estimates local method "REML"
/* return reml log-l in e(remll) NOT e(ll)
to ensure can't do inappropriate lrtest of fixed effects */
estimates scalar remll = `remll'
if "`remll_c'" != "" {
estimates scalar remll_c = `remll_c'
if (e(remll) < e(remll_c)) | (`tau2' <= 0) {
estimates scalar chi2_c = 0
}
else {
estimates scalar chi2_c = 2*(e(remll)-e(remll_c))
}
}
}
if "`noconstant'" == "" & "`notaucomp'"=="" & "`xvars'" != "" {
estimates scalar tau2_0 = `tau2_0'
}
/* overall F (or chi2) test */
if "`xvars'" != "" {
qui test `xvars'
if "`r(chi2)'" != "" {
estimates scalar chi2 = r(chi2)
}
else {
estimates scalar F = r(F)
}
}
estimates local predict "metareg_p"
estimates local depvar "`y'"
estimates local cmd "metareg"
Display, `level' `eform' `tau2test'
if "`graph'" != "" {
if `: word count `xvars'' != 1 {
di as error in smcl ///
"graph option works only with a single {it:indepvar}"
}
else Graph `y' `xvars' `v' `w' `randomsize'
}
end
program define Graph
version 8.2
args y xvars v w randomsize
tempvar fit
qui predict `fit'
if "`randomsize'" == "" {
local size `w'
}
else {
local size 1/`v'
}
local yti : variable label `y'
if "`yti'" == "" {
local yti `y'
}
scatter `y' `xvars' [aw=`size'], msymbol(oh) ///
|| line `fit' `xvars', sort ///
||, legend(off) ytitle( `yti')
end
program define Display
version 7
syntax [, Level(int $S_level) EForm Tau2test q]
tempname pval i2
if "`eform'" == "eform" {
local eform eform(exp(b))
}
scalar `pval' = chi2tail(e(df_Q), e(Q))
di as txt _n "Meta-regression" /*
*/ _col(55) "Number of obs" _col(70) "= " as res %7.0f e(N)
di as res "`e(method)'" /*
*/ as txt " estimate of between-study variance" /*
*/ _col(55) "tau2" _col(70) "=" as res %8.4g e(tau2)
di as txt "% residual variation due to heterogeneity" /*
*/ _col(55) "I-squared_res" _col(70) "= " as res %6.2f 100 * e(I2) "%"
if "`e(tau2_0)'" != "" {
di as txt "Proportion of between-study variance explained" /*
*/ _col(55) "Adj R-squared " _col(70) "= " _c
di as res %6.2f 100 * ( 1 - e(tau2) / e(tau2_0) ) "%"
}
/* Overall model fit */
if e(df_m) > 1 {
di as txt "Joint test for all covariates" _c
if "`e(df_r)'" == "" {
di as txt _col(55) "Model chi2(" as res e(df_m) as txt ")" /*
*/ _col(70) "= " as res %7.2f e(chi2)
}
else {
di as txt _col(55) "Model F(" as res e(df_m) as txt "," /*
*/ as res e(df_r) as txt ")" _col(70) "= " /*
*/ as res %7.2f e(F)
}
}
di as res "With" _c
if "`e(q_KH)'" == "" {
di as res "out" _c
}
di as txt " Knapp-Hartung modification" _c
if e(df_m) > 1 {
if "`e(df_r)'" == "" {
di as txt _col(55) "Prob > chi2" _col(70) "= " /*
*/ as res %7.4f chi2tail( e(df_m), e(chi2) )
}
else {
di as txt _col(55) "Prob > F" _col(70) "= " /*
*/ as res %7.4f Ftail( e(df_m), e(df_r), e(F) )
}
}
else {
di /* end line */
}
estimates display, level(`level') `eform'
if "`tau2test'" != "" {
di as txt "Test for residual between-study variance (of tau2=0)" /*
*/ _col(55) "Q_res (" as res %1.0f e(df_Q) as txt " df)" /*
*/ _col(70) "= " as res %7.2f e(Q)
di as txt _col(55) "Prob > Q_res" _col(70) "= " as res %7.4f `pval'
if "`e(method)'" == "REML" {
scalar `pval' = chi2tail(1, e(chi2_c))*0.5
if e(chi2_c) <=0 {
scalar `pval'= 1
}
di in smcl as txt "Likelihood-ratio test of tau2=0: " /*
*/ as txt "{help j_chibar:chibar2(01) =}" as res %6.2f e(chi2_c) /*
*/ as txt " Prob > chibar2 = " as res %7.4f `pval'
}
}
end
program define Permute, rclass
version 7
syntax, y(varname numeric) xvars(varlist numeric) wsvar(varname numeric) ///
permute(string) nobs(integer) [noconstant bsest(string) knapphartung z ///
level(passthru) touse(varname numeric)]
gettoken reps 0 : permute, parse(",")
confirm integer number `reps'
if `reps' < 1 {
di as err "permute() must be a positive integer"
exit 198
}
syntax [, Univariable Detail Verbose NOISily Joint(string) * ]
if "`verbose'" != "" { /* verbose is undoc'd synonym for detail */
local detail detail
}
if "`xvars'" == "" {
di as error "permute() option not allowed when no covariates"
exit 198
}
if "`joint'" != "" & "`univariable'" != "" {
di as error "options univariable and joint() cannot both be specified"
exit 198
}
if `:word count `xvars'' == 1 {
local univariable /* ensure unset if only one xvar */
}
if "`univariable'" == "" {
di as txt _n(1) "Monte Carlo permutation test for meta-regression"
}
else {
di as txt _n(1) in smcl /*
*/ "Monte Carlo permutation test for {it:single covariate} meta-regressions"
}
if "`bsest'" == "" {
local bsest "mm" /* mm is default for permutation test */
}
if "`bsest'" == "mm" {
di as res _n "Moment-based " _c
}
else {
di as res _n "REML " _c
}
di as txt "estimate of between-study variance"
if "`knapphartung'" == "" {
local z "z" /* z is default for for permutation test */
di as res "Without " _c
}
else {
di as res "With " _c
}
di as txt "Knapp & Hartung modification to standard errors"
if "`noconstant'" != "" {
di as error "Permutation test inappropriate with no constant"
exit 198
}
local permopts `univariable' `noconstant' `bsest' `knapphartung' `z'
preserve
qui keep if `touse'
foreach x of varlist `xvars' {
local explist "`explist' `x'=r(z_`x')"
local xlist "`xlist' `x'"
}
/* parse varlists in joint() option separated by \ / or | */
local jt `joint'
local nj 0
while "`jt'" != "" {
gettoken tmp jt : jt, parse("\/|")
if strpos("\/|", "`tmp'") {
continue
}
local 0 `tmp'
syntax varlist
local nj = `nj' + 1
local joint`nj' `varlist'
di as txt "joint`nj' : " as res "`joint`nj''"
local explistj "`explistj' joint`nj'=r(chi2`nj')"
local jlist "`jlist' joint`nj'"
}
tempvar n
qui gen `n' = _n if `touse'
tempfile pm pmadj
if "`noisily'" =="" {
local quietly quietly
/* quietly permute then replay (if detail specified)
to suppress full -permute- header */
}
`quietly' permute `n' /*
*/ "metareg_pm `y' `wsvar' `n', xvars(`xvars') joint(`joint') `permopts'" /*
*/ `explist' `explistj', /*
*/ reps(`reps') saving(`pm') replace `options'
tempname R C P B
matrix `R' = r(reps)
matrix `C' = r(c)
matrix `P' = `C' * inv(diag(`R'))
local nx : word count `xvars'
forvalues i = 1/`nx' {
local minreps = min(`reps',`R'[1,`i'])
}
if `minreps' != `reps' {
di as txt "WARNING: some permutations returned missing values" _c
di as txt "See detail below:"
local detail detail
}
/* to get adjusted p-values from -permute-,
replace z-statistics by their largest absolute values
for each permutation */
quietly {
use `pm', clear
tempvar max
gen `max' = 0
foreach v of varlist `xlist' {
replace `max' = abs(`v') if abs(`v') > `max'
}
foreach v of varlist `xlist' {
replace `v' = `max'
}
drop `max'
save `pmadj', replace
permute `xlist' `jlist' using `pmadj', `level'
} // end quietly
matrix `B' = r(b)
matrix `R' = `R' \ r(reps)
matrix `C' = `C' \ r(c)
matrix `P' = `P' \ r(c) * inv(diag(r(reps)))
foreach M in `R' `C' `P' {
matrix rownames `M' = unadj multadj
if "`joint'" != "" {
/* set matrix entries for adjusted joint tests (N/A) to missing */
matrix `M'[2, colnumb(`M',"joint1")] = J(1,`nj',.)
}
}
/* clear results from last permutation so not available to user */
estimates clear
restore
/*** Display results of permutation test ****/
if `nx' == 1 { /* only one xvar */
local sep = sqrt(`P'[1,1]*(1-`P'[1,1])/`reps')
if `sep' == 0 {
local sep .
}
di as txt _n _col(9) "Number of obs =" as res %8.0f _N
di as txt _col(9) "Permutations =" as res %8.0f `reps'
di in smcl as txt "{hline 13}{c TT}{hline 17}"
di in smcl as txt /*
*/ %12s "`y'" " {c |}" _s(1) /*
*/ %6s "p" _s(4) /*
*/ %6s "SE(p)"
di in smcl as txt "{hline 13}{c +}{hline 17}"
di in smcl as txt /*
*/ %12s abbrev(`"`xvars'"',10) /*
*/ " {c |}" _s(2) /*
*/ as result %7.3f `P'[1,1] _s(1) /*
*/ as result %7.4f `sep'
di in smcl as txt "{hline 13}{c BT}{hline 17}"
}
else { /* more than one xvar */
/* Table Head */
di as txt _n "P-values unadjusted and adjusted for multiple testing"
di as txt _n _col(14) "Number of obs =" as res %8.0f `nobs'
di as txt _col(14) "Permutations =" as res %8.0f `reps'
di in smcl as txt "{hline 13}{c TT}{hline 22}"
di in smcl as txt _s(13) "{c |}" _s(12) "P"
di in smcl as txt /*
*/ %12s abbrev("`y'",10) " {c |}" /*
*/ %11s "Unadjusted" %11s "Adjusted"
di in smcl as txt "{hline 13}{c +}{hline 22}"
foreach x of local xvars {
local i = `i' + 1
di in smcl as txt /*
*/ %12s abbrev(`"`x'"',10) /*
*/ " {c |}" _s(4) /*
*/ as result %7.3f `P'[1,`i'] _s(4) /*
*/ %7.3f `P'[2,`i']
}
if "`joint'" != "" {
di in smcl as txt "{hline 13}{c +}{hline 22}"
forvalues j = 1/`nj' {
di in smcl as txt /*
*/ %12s "joint`j'" /*
*/ " {c |}" _s(4) /*
*/ as result %7.3f `P'[1,`nx'+`j']
}
}
/* Table Foot */
di in smcl as txt "{hline 13}{c BT}{hline 22}"
/* largest SE(p) = sqrt( max( p(1-p)/n ) ) */
local maxvarp 0
forvalues i = 1/2 {
forvalues j = 1/`=colsof(`P')'{
local maxvarp = max( `maxvarp', ///
`P'[`i',`j'] * (1 - `P'[`i',`j']) / `R'[`i',`j'] )
}
}
if `maxvarp' == 0 {
local maxvarp .
}
di as txt "largest Monte Carlo SE(P) =" /*
*/ as res %7.4f sqrt(`maxvarp')
}
/* Detailed results if requested, as displayed by -permute- */
if "`detail'" != "" {
di as txt in smcl _n(2) "{title:Unadjusted}"
permute `xlist' `jlist' using `pm', `level'
if `nx' > 1 {
di as txt in smcl _n(2) "{title:Adjusted for multiple testing}"
permute `xlist' using `pmadj', `level'
}
}
di as txt _n "WARNING:"
di as txt "Monte Carlo methods use random numbers, so results may differ between runs."
di as txt "Ensure you specify enough permutations to obtain the desired precision."
return scalar N = `nobs'
return matrix reps = `R'
return matrix c = `C'
return matrix p = `P'
return matrix b = `B'
end
program define metareg_orig
*! v1.06 copyright Stephen Sharp January 1998 STB-42 sbe23
version 5.0
local varlist "req ex min(1)"
local if "opt"
local in "opt"
local options "WSSe(string) WSVar(string) BSest(string) TOLeran(integer 4) Level(integer $S_level)"
local options "`options' NOITer ORIGinal" /* original added by RMH */
parse "`*'"
if "`wsse'"=="" & "`wsvar'"=="" {
di in re "Must specify a variable containing estimate of precision"
di in re "within each trial, using either wsse() or wsvar() option"
exit 198
}
if "`wsse'"~="" & "`wsvar'"~=""{
confirm variable `wsse'
confirm variable `wsvar'
local i=1
while `i'<=_N {
if abs(`wsse[`i']'^2-`wsvar[`i']')>0.00001 {
di in re "Within study variance should be square of within study standard error"
exit 198
}
local i=`i'+1
}
}
if "`wsvar'"~="" {
confirm variable `wsvar'
}
if "`wsse'"~="" {
confirm variable `wsse'
tempvar wsvar
qui gen `wsvar'=`wsse'^2
}
if "`bsest'"=="" {
local bsest "reml"
}
if "`bsest'"~="reml" & "`bsest'"~="ml" & "`bsest'"~="eb" & "`bsest'"~="mm" & "`bsest'"~="" {
di in re "Between study variance estimation method invalid:"
di in re "should be either reml, ml, eb or mm"
exit 198
}
if "`noiter'"~="" & "`bsest'"=="mm" {
di in bl "Warning: mm is a non-iterative method, noiter option ignored"
}
parse "`varlist'", parse(" ")
local y "`1'"
mac shift
local xvars "`*'"
tempvar touse
preserve
qui {
mark `touse' `if' `in'
markout `touse' `y' `xvars'
keep if `touse'
}
qui regress `y' `xvars'
local p=_result(3)
local N=_result(1)
if "`bsest'"=="mm" {
tempvar wt ypred numi one
qui {
gen `wt'=`wsvar'^-1
regress `y' `xvars' [aw=`wt']
predict `ypred'
gen `one'=1
tempname X C Xt C1 XtC1 A A1 A1XtC1 C1X B
mkmat `one' `xvars', matrix(`X')
matrix `C'=J(`N',`N',0)
local i=1
while `i'<=_N {
matrix `C'[`i',`i']=`wsvar'[`i']
local i=`i'+1
}
mat `Xt'=`X''
mat `C1'=inv(`C')
mat `XtC1'=`Xt'*`C1'
mat `A'=`XtC1'*`X'
mat `A1'=inv(`A')
mat `A1XtC1'=`A1'*`XtC1'
mat `C1X'=`C1'*`X'
mat `B'=`C1X'*`A1XtC1'
local trB=trace(`B')
summ `wt'
local denom=_result(18)-`trB'
gen `numi'=`wt'*((`y'-`ypred')^2)
summ `numi'
local num=max(_result(18)-(`N'-(`p'+1)),0)
local newtsq=`num'/`denom'
}
}
if "`bsest'"~="mm" {
local tsq=0.1
local newtsq=0
local j=1
while abs(`tsq'-`newtsq')>=10^(-`toleran') {
tempvar wt ypred wtsq numi
local tsq=`newtsq'
if "`noiter'"=="" {
di in gr "Iteration " `j' ": tau^2 = " in ye `tsq'
}
qui {
gen `wt'=(`wsvar'+`tsq')^-1
regress `y' `xvars' [aw=`wt']
predict `ypred'
gen `wtsq'=`wt'^2
}
if "`bsest'"=="reml" {
qui {
gen `numi'=(`wtsq')*(((`N'/(`N'-(`p'+1)))*((`y'-`ypred')^2))-`wsvar')
summ `numi'
local num=max(_result(18),0)
summ `wtsq'
local denom=_result(18)
local newtsq=`num'/`denom'
}
}
if "`bsest'"=="ml" {
qui {
gen `numi'=(`wtsq')*(((`y'-`ypred')^2)-`wsvar')
summ `numi'
local num=max(_result(18),0)
summ `wtsq'
local denom=_result(18)
local newtsq=`num'/`denom'
}
}
if "`bsest'"=="eb" {
qui {
gen `numi'=(`wt')*(((`N'/(`N'-(`p'+1)))*((`y'-`ypred')^2))-`wsvar')
summ `numi'
local num=max(_result(18),0)
summ `wt'
local denom=_result(18)
local newtsq=`num'/`denom'
}
}
local j=`j'+1
}
}
tempvar wt
qui {
gen `wt'=(`wsvar'+`newtsq')^-1
summ `wt'
}
local sumwt=_result(18)
#delimit ;
di _n
in gr "Meta-analysis regression"
_col(56) "No of studies = " in ye `N' _n
in gr _col(56) "tau^2 method " in ye "`bsest'" _n
in gr _col(56) "tau^2 estimate = " in ye %6.5g `newtsq' _n ;
#delimit cr
if "`bsest'"=="ml" | "`bsest'"=="reml" | "`bsest'"=="eb" {
di in bl "Successive values of tau^2 differ by less than 10^-"`toleran' " :convergence achieved"
}
qui regress `y' `xvars' [aw=`wt']
local scpar=(`sumwt'*(_result(9)^2))/_result(1)
local scpar1=(1/`scpar')
matrix V=get(VCE)
matrix b=get(_b)
matrix v=`scpar1'*V
mat post b v
mat mlout, level(`level')
global S_1 = `N'
global S_2 = `newtsq'
restore
end
exit

113
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/*
*! metareg dialog version 1.0.1, 13 May 2003, T. J. Steichen, steichen@triad.rr.com
*! for metareg version 1.06, Jan 1998, Stephen Sharp, stephen.j.sharp@gsk.com
Meta-analysis regression
------------------------
Syntax: metareg y varlist [if exp] [in range] , { wsse(varname)| wsvar(varname) }
[ bsest(string) toleran(#) level(#) noiter ]
To install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "Meta-analysis Regression (meta&reg)" "db metareg"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
INCLUDE header
HELP hlp1, view("help metareg")
RESET res1, label("Reset")
DIALOG main, label("metareg 1.06 - Meta-analysis Regression") tabtitle("Main")
BEGIN
TEXT tx_theta 10 5 110 ., ///
label("Dependent (y):")
VARNAME vn_theta 110 5 230 ., ///
label("Var for theta")
CHECKBOX cb_id 10 35 100 ., ///
label("Covariates:") ///
onclickon(main.vl_covar.enable) ///
onclickoff(main.vl_covar.disable)
VARLIST vl_covar 110 35 230 ., ///
label("Vars for covariates")
GROUPBOX gb_ws 5 60 340 _ht3h, ///
label("Within-study Varibility:")
RADIO r_se 15 80 85 ., ///
label("Std. Error") first ///
option("wsse")
RADIO r_var 155 80 75 ., ///
label("Variance") last ///
option("wsvar")
TEXT tx_ws 10 100 110 ., ///
label("Variable name:")
VARNAME vn_ws 110 100 230 ., ///
label("WS Variable")
GROUPBOX gb_bsest 5 130 175 _ht3h, ///
label("BS Method")
RADIO r_reml 15 150 55 ., ///
label("ReML") first ///
option("bsest(reml)")
RADIO r_ml 15 170 55 ., ///
label("ML") middle ///
option("bsest(ml)")
RADIO r_eb 85 150 85 ., ///
label("EmBayes") middle ///
option("bsest(eb)")
RADIO r_mm 85 170 85 ., ///
label("Moments") last ///
option("bsest(mm)")
CHECKBOX cb_noiter 210 135 95 ., ///
label("No Iteration") ///
option("noiter")
CHECKBOX cb_level 210 155 85 ., ///
label("CI Level:") ///
onclickon(main.ed_level.enable) ///
onclickoff(main.ed_level.disable)
EDIT ed_level 300 @ 40 ., ///
label("Level") ///
numonly default(95) ///
option("level")
CHECKBOX cb_tol 210 175 85 ., ///
label("Tolerance:") ///
onclickon(main.ed_tol.enable) ///
onclickoff(main.ed_tol.disable)
EDIT ed_tol 300 @ 40 ., ///
label("Tolerance") ///
numonly default(4) ///
option("toleran")
END
INCLUDE ifin
PROGRAM command
BEGIN
put "metareg "
varlist main.vn_theta [main.vl_covar]
INCLUDE _ifin_pr
beginoptions
if main.r_se {
put "wsse("
}
if main.r_var {
put "wsvar("
}
put main.vn_ws
put ")"
option radio(main r_reml r_ml r_eb r_mm)
option main.cb_noiter
optionarg main.ed_level
optionarg main.ed_tol
endoptions
END

368
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{smcl}
{* 31oct2008}{...}
{cmd:help metareg}{right: ({browse "http://www.stata-journal.com/article.html?article=up0023":SJ8-4: sbe23_1})}
{hline}
{title:Title}
{p2colset 5 16 18 2}{...}
{p2col :{hi:metareg} {hline 2}}Meta-analysis regression (revised){p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 15 2}
{cmd: metareg}
{it:{help varname:depvar}} [{it:{help varlist:indepvars}}]
{ifin} {cmd:wsse(}{it:varname}{cmd:)}
[, {cmdab:ef:orm}
{cmdab:g:raph}
{cmdab:ra:ndomsize}
{cmdab:nocons:tant}
{cmd:mm} {cmd:reml} {cmd:eb}
{cmdab:k:napphartung} {cmd:z}
{cmdab:tau:2test}
{cmdab:l:evel(}{it:#}{cmd:)}
{cmdab:perm:ute(}{it:#} [{cmd:,} {cmdab:u:nivariable} {cmdab:d:etail}
{cmdab:j:oint(}{it:varlist1} [{cmd:|} {it:varlist2} ...]{cmd:)}]{cmd:)}
{cmd:log}
{it:maximize_options}]
{pstd}
{cmd:by} can be used with {cmd:metareg}; see {manhelp by D}.
{title:Description}
{pstd}
{cmd:metareg} performs random-effects meta-regression
using aggregate-level data.
{pstd}
From a more abstract perspective, it extends {helpb vwls}
by estimating an extra additive component of variance tau2:
{p 8 17 2}
y_i = a + B*x_i + u_i + e_i
{pstd} where a is a constant, u_i is a normal error term with known standard deviations
wsse_i that may vary across units, and e_i is a normal error with variance
tau2 to be estimated, assumed equal across units. This is a similar model to
those fit by the {helpb xt} commands, except that the within-unit data have
been summarized by an effect estimate and its standard error for each unit i.
{title:Options}
{phang}{cmd:wsse(}{it:varname}{cmd:)} specifies the variable
containing the standard error of {it:depvar} within each study
({ul:w}ithin-{ul:s}tudy {ul:s}tandard {ul:e}rror). All values of {it:varname}
must be greater than zero. {cmd:wsse()} is required.
{phang}{cmd:eform} indicates to output the exponentiated form of the
coefficients and to suppress reporting of the constant. This option may
be useful when {it:depvar} is the logarithm of a ratio measure, such as a log
odds-ratio or a log risk-ratio.
{phang}{cmd:graph} requests a line graph of fitted values
plotted against the first covariate in {it:indepvars},
together with the estimates from each study represented by circles.
By default, the circle sizes depend on the precision of each estimate
(the inverse of its within-study variance),
which is the weight given to each study in the fixed-effects model.
{phang}{cmd:randomsize} is for use with the {cmd:graph} option. It
specifies that the size of the circles will depend on the weights in the
random-effects model rather than the precision of each estimate. These
random-effects weights depend on the estimate of tau2.
{pstd} The remaining options will mainly be of interest to more advanced users:
{phang}{cmd:noconstant} suppresses the constant term (intercept).
This is rarely appropriate in meta-regression.
{pstd} The {cmd:mm}, {cmd:reml}, and {cmd:eb} options are alternatives
that specify the method of estimation
of the additive (between-study) component of variance tau2.
{phang}{cmd:mm} specifies the use of method of moments to estimate the
additive (between-study) component of variance tau2; this is a generalization
of the DerSimonian and Laird (1986) method commonly used for random-effects
meta-analysis. For speed, this is the default when the {cmd:permute()} option
is specified, because it is the only noniterative method.
{phang}{cmd:reml} specifies the use of residual maximum likelihood (REML) to estimate the additive
(between-study) component of variance tau2. This is the default unless the
{cmd:permute()} option is specified. This revised version uses
Stata's maximum likelihood facilities to maximize the REML
log likelihood. It will therefore not give identical results to the previous
version of {cmd:metareg}, which used an approximate iterative method.
{phang}{cmd:eb} specifies the use of the "empirical Bayes" method
to estimate tau2 (Morris 1983).
{phang}{cmd:knapphartung} makes a modification to the variance of the
estimated coefficients suggested by Knapp and Hartung (2003), accompanied by
the use of a t distribution in place of the standard normal distribution when
calculating p-values and confidence intervals. This is the default unless the
{cmd:permute()} option is specified.
{phang}{cmd:z} requests that the {cmd:knapphartung} modification not be
applied and that the standard normal distribution be used to calculate
p-values and confidence intervals. This is the default when the
{cmd:permute()} option is specified with a fixed-effects model.
{phang}{cmd:tau2test} adds to the output two tests of tau2 = 0. The first is
based on the residual heterogeneity statistic, Q_res. The second (not
available if the {cmd:mm} option is also
specified) is a likelihood-ratio test based on the REML log likelihood. These
are two tests of the same null hypothesis (the fixed-effects model with tau2
= 0), but the alternative hypotheses are different, as are the distributions of
the test statistics under the null, so close agreement of the two tests is not
guaranteed. Both tests are typically of little interest because it is more
helpful to quantify heterogeneity than to test for it.
{phang} {opt level(#)} specifies the confidence level, as a percentage, for
confidence intervals. The default is {cmd:level(95)} or as set by
{helpb set level}.
{phang} {cmd:permute(}...{cmd:)} calculates p-values by using a Monte Carlo permutation
test. See {help metareg##permute:Option for permutation test} for more information about the option.
{phang}{cmd:log} requests the display of the iteration log during estimation of
tau2. This is ignored if the {cmd:mm} option is specified, because this uses a
noniterative method.
{phang}{it:maximize_options} are ignored unless estimation of tau2 is by REML.
These options control the maximization process; see {helpb maximize}. You
should never need to specify them; they are supported only in case problems in
the REML estimation of tau2 are ever reported or suspected.
{marker permute}{...}
{title:Option for permutation test}
{pstd} The {cmd:permute()} option calculates p-values by using a Monte
Carlo permutation test, as recommended by Higgins and
Thompson (2004). To address multiple testing, {cmd:permute()} also calculates
p-values for the most- to least-significant covariates, as the same
authors also recommend.
{pstd}
The syntax of {cmd:permute()} is
{p 8 15 2}{cmd:permute(}{it:#}
[{cmd:,}
{cmd:univariable}
{cmd:detail}
{cmd:joint(}{it:varlist1} [{cmd:|} {it:varlist2} ...]{cmd:)}]{cmd:)}
{pstd}
where {it:#} is required and specifies the number of random permutations to
perform. Larger values give more precise p-values but take longer.
{pstd}
There are three suboptions:
{phang}{cmd:univariable} indicates that p-values should be calculated for a
series of single covariate meta-regressions of each covariate in {it:varlist}
separately, instead of a multiple meta-regression of all covariates in
{it:varlist} simultaneously.
{phang}{cmd:detail} requests more detailed output in the style given by
{helpb permute}.
{phang}{cmd:joint(}{it:varlist1} [{cmd:|} {it:varlist2} ...]{cmd:)}
specifies that a permutation p-value should also be computed for a joint
test of the variables in each {help varlist}.
{pstd}The {cmd:eform}, {cmd:level()}, and {cmd:z} options have
no effect when the {cmd:permute()} option is specified.
{title:Syntax of predict}
{p 4 4}
The syntax of {helpb predict} following {cmd:metareg} is
{p 8 15 2}{cmd:predict} [{it:type}] {it:newvar} {ifin} [{cmd:,} {it:statistic}]
{pstd}
where {it:statistic} is
{p 8 25}{cmd:xb}{space 11}fitted values; the default{p_end}
{p 8 25}{cmd:stdp}{space 9}standard error of the prediction{p_end}
{p 8 25}{cmd:stdf}{space 9}standard error of the forecast{p_end}
{p 8 25}{cmd:u}{space 12}predicted random effects{p_end}
{p 8 25}{cmdab:usta:ndard}{space 4}standardized predicted random effects{p_end}
{p 8 25}{cmd:xbu}{space 10}prediction including random effects{p_end}
{p 8 25}{cmd:stdxbu}{space 7}standard error of xbu{p_end}
{p 8 25}{cmdab:h:at}{space 10}leverage (diagonal elements of hat matrix)
{pstd}
These statistics are available both in and out of sample;
type {cmd:predict ... if e(sample) ...} if wanted only for the estimation sample.
{title:Options for predict}
{phang}{cmd:xb}, the default, calculates the linear prediction, x_i*b, that is, the fitted values excluding the random effects.
{phang}{cmd:stdp} calculates the standard error of the prediction
(the standard error of the fitted values excluding the random effects).
{phang}{cmd:stdf} calculates the standard error of the forecast.
This gives the standard deviation of the predicted distribution
of the true value of {it:depvar} in a future study,
with the covariates given by {it:varlist}.
{break}stdf^2 = stdp^2 + tau2.
{phang}{cmd:u} calculates the predicted random effects, u_i. These are the
best linear unbiased predictions of the random effects, also known as
the empirical Bayes (or posterior mean) estimates of the random effects,
or as shrunken residuals.
{phang}{cmd:ustandard} calculates the standardized predicted random effects,
i.e., the predicted random effects, u_i,
divided by their (unconditional) standard errors.
These may be useful for diagnostics and model checking.
{phang}{cmd:xbu} calculates the prediction including random effects, a +
B*x_i + u_i, also known as the empirical Bayes estimates of the effects for
each study.
{phang}{cmd:stdxbu} calculates the standard error of the prediction including
random effects.
{phang}{cmd:hat} calculates the leverages (the diagonal elements of the
projection hat matrix).
{title:Saved results}
{pstd}When the {cmd:permute()} option is not specified, {cmd:metareg} saves
the following in {cmd:e()}:
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Scalars}{p_end}
{synopt:{cmd:e(N)}}number of observations{p_end}
{synopt:{cmd:e(df_m)}}model degrees of freedom{p_end}
{synopt:{cmd:e(df_Q)}}degrees of freedom for test of Q=0{p_end}
{synopt:{cmd:e(df_r)}}residual degrees of freedom (if t tests used){p_end}
{synopt:{cmd:e(remll)}}REML log likelihood{p_end}
{synopt:{cmd:e(chi2_c)}}chi^2 for comparison test{p_end}
{synopt:{cmd:e(F)}}model F statistic{p_end}
{synopt:{cmd:e(tau2)}}estimate of tau2{p_end}
{synopt:{cmd:e(Q)}}Cochran's Q{p_end}
{synopt:{cmd:e(I2)}}I-squared{p_end}
{synopt:{cmd:e(q_KH)}}Knapp-Hartung variance modification factor{p_end}
{synopt:{cmd:e(remll_c)}}REML log likelihood, comparison model{p_end}
{synopt:{cmd:e(tau2_0)}}tau2, constant-only model{p_end}
{synopt:{cmd:e(chi2)}}model chi^2{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Macros}{p_end}
{synopt:{cmd:e(cmd)}}{cmd:metareg}{p_end}
{synopt:{cmd:e(predict)}}program used to implement {cmd:predict}{p_end}
{synopt:{cmd:e(wsse)}}name of {cmd:wsse()} variable{p_end}
{synopt:{cmd:e(depvar)}}name of dependent variable{p_end}
{synopt:{cmd:e(method)}}{cmd:REML}, {cmd:Method of moments}, or
{cmd:Empirical Bayes}{p_end}
{synopt:{cmd:e(properties)}}{cmd:b V}{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Matrices}{p_end}
{synopt:{cmd:e(b)}}coefficient vector{p_end}
{synopt:{cmd:e(V)}}variance-covariance matrix of estimators{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Functions}{p_end}
{synopt:{cmd:e(sample)}}marks estimation sample{p_end}
{p2colreset}{...}
{title:Examples}
{phang}{cmd:. metareg logrr latitude, wsse(selogrr) eform}
{phang}{cmd:. metareg logrr latitude, wsse(selogrr) graph}
{phang}{cmd:. metareg smd abstract duration itt, wsse(sesmd) permute(10000)}
{phang}{cmd:. metareg smd abstract duration itt, wsse(sesmd) permute(1000,}
{cmd: univariable)}
{phang}{cmd:. xi: metareg logor i.group, wsse(selogor) permute(1000, joint(i.group))}
{title:Note}
{pstd} {cmd:metareg} is programmed as a Stata estimation command and so
supports many of the commands listed under {helpb estcom} and
{helpb postest} (except when the {cmd:permute()} option is used). One
deliberate exception is {helpb lrtest}, which is not appropriate after
{cmd:metareg} (because the REML log likelihood cannot be used to compare models
with different fixed effects, while the method of moments is not based on a
likelihood). For this reason, when the REML method is used, the iteration log
showing the log likelihood is suppressed by default; specify
the {cmd:log} option if you wish to see it.
{title:References}
{phang}DerSimonian, R., and N. Laird. 1986. Meta analysis in clinical trials.
{it:Controlled Clinical Trials} 7: 177-188.
{phang} Higgins, J. P. T, and S. G. Thompson. 2004. Controlling the risk of
spurious findings from meta-regression. {it:Statistics in Medicine} 23:
1663-1682.
{phang} Knapp, G., and J. Hartung. 2003. Improved tests for a random effects
meta-regression with a single covariate. {it:Statistics in Medicine} 22:
2693-2710.
{phang} Morris, C. N. 1983. Parametric empirical Bayes inference: Theory and
applications. {it:Journal of the American Statistical Association} 78: 47-55.
{phang} Sharp, S. 1998. sbe23: Meta-analysis regression. {it:Stata Technical Bulletin} 42: 16-22. Reprinted in {it:Stata Technical Bulletin Reprints}, vol. 7, pp. 148-155. College Station, TX: Stata Press.
{title:Author}
{pstd}Roger M. Harbord{p_end}
{pstd}Department of Social Medicine{p_end}
{pstd}University of Bristol, UK{p_end}
{pstd}{browse "mailto:roger.harbord@bristol.ac.uk":roger.harbord@bristol.ac.uk}{p_end}
{title:Acknowledgments}
{pstd}
This is a substantial revision of the original version of {cmd:metareg}
written by Stephen Sharp (1998), who gave his permission to release this
version under the same name and to incorporate his code. Julian Higgins gave
advice on the permutation test. Aijing Shang tested early versions and made
helpful suggestions. Portions of the new code borrow ideas from official
Stata commands such as {cmd:nbreg}, and I thank StataCorp for making such code
visible to the user.
{pstd}
A dialog box, written by Thomas J. Steichen, is available for the original
version of the {cmd:metareg} command.
{title:Also see}
{psee}
Article: {it:Stata Journal}, volume 8, number 4: {browse "http://www.stata-journal.com/article.html?article=up0023":sbe23_1},{break}
{it:Stata Technical Bulletin} 42: {browse "http://www.stata.com/products/stb/journals/stb42.pdf":sbe23}
{psee}
Manual: {hi:[R] meta},{break}
{hi:[R] permute}
{psee}
Online: {manhelp vwls R}, {manhelp permute R}, {helpb meta} (if installed),
{helpb metan} (if installed), {help meta_dialog} (if installed)
{p_end}

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*! v 1.1 26Oct2004
*! Roger Harbord
/*
log-likelihood evaluator (method d0) for metareg v2.
Estimates tau2 by REML.
Variables :
$ML_y1 holds the y-vector of study estimates,
$ML_y2 holds the within-study variances
$REML_x holds the covariates
$REML_nocons is flag "" or "noconstant"
*/
program define metareg_ll
version 7
args todo b lnf
tempvar lnfj v r
tempname R tau2
mleval `tau2' = `b', scalar
quietly {
gen double `v' = $ML_y2 +`tau2' /* v_i = sigma_i^2 + tau^2 */
capture regress $ML_y1 $REML_x [iw=1/`v'] if $ML_samp, $REML_nocons
if _rc == 0 {
predict double `r' if $ML_samp, resid
gen double `lnfj' = `r'^2/`v' +log(`v')
mlsum `lnf' = `lnfj'
matrix accum R = $REML_x [iw=1/`v'] if $ML_samp, $REML_nocons
/* R = X'V^-1X is reml correction to -2logL */
scalar `lnf' = -0.5 * ( `lnf' +log( det(R) ) )
}
else {
scalar `lnf' = .
}
}
end

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*! version 1.21 March 23, 2007 @ 17:05:47
*! Roger Harbord
/*
code based on example in _pred_se.hlp
v1.21 fixed stdxbu
*/
program define metareg_p
version 7
local myopts "STDF U USTAndard XBU STDXBU Hat"
_pred_se "`myopts'" `0' /* handles xp and stdp */
if `s(done)' {
exit
}
local typ `s(typ)'
local varn `s(varn)'
local 0 `"`s(rest)'"'
syntax [if] [in] [, `myopts' noOFFset]
/* concatenate switch options together */
local type "`stdf'`u'`ustandard'`xbu'`stdxbu'`hat'"
/* quickly process default case */
if "`type'"=="" {
di as txt "(option xb assumed; fitted values)"
_predict `typ' `varn' `if' `in', xb `offset'
exit
}
/* mark sample */
marksample touse
if "`type'"=="u" | "`type'"=="xbu" | "`type'" == "ustandard" {
tempvar xb
qui _predict double `xb' if `touse', xb `offset'
}
if "`type'" == "stdf" | "`type'" == "stdxbu" | "`type'" == "ustandard" /*
*/ | "`type'" == "hat" {
tempvar stdp
qui _predict double `stdp' if `touse', stdp `offset'
}
if "`type'" == "u" | "`type'" == "xbu" | "`type'" == "stdxbu" /*
*/ | "`type'" =="ustandard" | "`type'" =="hat" {
tempvar wsvar
if "`e(wsvar)'" !="" {
qui gen `wsvar' = `e(wsvar)' if `touse'
}
else {
qui gen `wsvar' = `e(wsse)'^2 if `touse'
}
}
if "`type'" == "u" | "`type'" == "xbu" | "`type'" == "stdxbu" {
tempvar B /* Bayes shrinkage factor */
qui gen double `B' = e(tau2) / ( e(tau2) + `wsvar' ) if `touse'
}
if "`type'" == "u" {
gen `typ' `varn' = `B' * ( `e(depvar)' - `xb' ) if `touse'
label var `varn' "Predicted random effects"
exit
}
if "`type'" == "xbu" {
gen `typ' `varn' = `B' * `e(depvar)' + (1-`B') * `xb' if `touse'
label var `varn' "Prediction including random effects"
exit
}
if "`type'" == "stdf" {
gen `typ' `varn' = sqrt( `stdp'^2 + e(tau2) ) if `touse'
label var `varn' "S.E. of the forecast"
exit
}
if "`type'" == "stdxbu" {
gen `typ' `varn' = sqrt( `B'^2 * ( `wsvar' + e(tau2) ) ///
+ (1-`B'^2) * `stdp'^2 ) if `touse'
label var `varn' "S.E. of prediction incl. random effects"
exit
}
if "`type'" == "ustandard" {
gen `typ' `varn' = ( `e(depvar)' - `xb' ) / /*
*/ sqrt( `wsvar' + e(tau2) - `stdp'^2 ) if `touse'
label var `varn' "Standardized predicted random effects"
exit
}
if "`type'" == "hat" {
gen `typ' `varn' = `stdp'^2 / ( `wsvar' + e(tau2) ) if `touse'
label var `varn' "Leverage"
exit
}
error 198
end

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*! v1.3.0 8Sep2005
/* added "joint" option to go with metareg.ado v2.3.1 */
program define metareg_pm, rclass
version 7
syntax varlist [if], xvars(varlist numeric) [ univariable joint(string) ] *
tokenize `varlist'
args y wsvar n
marksample touse
tempvar xp z zabs
tempname zi
foreach x of varlist `xvars' {
gen `: type `x'' `xp' = `x'[`n'] if `touse'
drop `x'
rename `xp' `x'
}
if "`univariable'" == "" { /* single multiple meta-regression */
metareg `y' `xvars' if `touse', wsvar(`wsvar') notaucomp `options'
foreach x of varlist `xvars' {
scalar `zi' = _b[`x'] / _se[`x']
return scalar z_`x' = `zi'
}
}
else { /* separate univariable meta-regressions */
foreach x of varlist `xvars' {
metareg `y' `x' if `touse', wsvar(`wsvar') notaucomp `options'
scalar `zi' = _b[`x'] / _se[`x']
return scalar z_`x' = `zi'
}
}
while "`joint'" != "" {
gettoken tmp joint : joint, parse("\/|")
if strpos("\/|", "`tmp'") {
continue
}
local 0 `tmp'
syntax varlist
local j = `j' + 1
qui test `varlist'
if "`r(chi2)'" != "" {
return scalar chi2`j' = r(chi2)
}
else {
return scalar chi2`j' = r(F) * r(df)
}
}
end

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/*
*! metatrim dialog version 1.0.2, 22 Jul 2003, T. J. Steichen, steichen@triad.rr.com
*! for metatrim version 1.0.5, 18 Oct 2002, T. J. Steichen, steichen@triad.rr.com
Nonparametric "trim and fill" analysis of publication bias in meta-analysis
---------------------------------------------------------------------------
syntax: metatrim varlist [if] [in]
[ , Vari CI REffect PRint Funnel EForm LEvel(real 95) ESTimat(R|L|Q)
GRaph IDvar(varname) FLIP MIXED SAve(filename [, REPLACE]) * ]
Install in User Statistics menu via Stata commands:
. window menu append item "stUserStatistics" "meta&trim ("Trim and Fill" Analysis)" "db metatrim"
. window menu refresh
To permanently install, place the commands in your -profile.do- file.
*/
VERSION 8.0
INCLUDE _std_small
DEFINE _dlght 230
INCLUDE header
HELP hlp1, view("help metatrim")
RESET res1, label("Reset")
DIALOG main, label(`"metatrim 1.0.5 - "Trim and Fill" Analysis"') tabtitle("Main")
BEGIN
GROUPBOX gb_data 10 5 330 85, ///
label("Type of Data")
RADIO r_se 15 20 100 ., ///
label("Theta, SE") first ///
onclickon(script main_se_on) ///
option(NONE)
RADIO r_var 115 @ 100 ., ///
label("Theta, Var") middle ///
onclickon(script main_var_on) ///
option("var")
RADIO r_ci 215 @ 110 ., ///
label("exp(Theta), CI") last ///
onclickon(script main_ci_on) ///
option("ci")
TEXT tx_se 15 40 320 ., ///
label("Vars for theta, se(theta), in that order")
VARLIST vl_se @ _ss @ ., ///
label("Vars for theta, se(theta)")
TEXT tx_var @ 40 320 ., ///
label("Vars for theta, var(theta), in that order")
VARLIST vl_var @ _ss @ ., ///
label("Vars for theta, var(theta)")
TEXT tx_ci @ 40 320 ., ///
label("Vars for exp(theta), CI_LL, CI_UL, in that order")
VARLIST vl_ci @ _ss @ ., ///
label("Vars for exp(theta), CI_LL, CI_UL")
GROUPBOX gb_estimat 10 95 90 80, ///
label("Estimate")
RADIO r_linear 15 110 80 ., ///
label("Linear") first ///
option(NONE)
RADIO r_run @ 130 @ ., ///
label("Run") middle ///
option("est(run)")
RADIO r_quadratic @ 150 @ ., ///
label("Quadratic") last ///
option("est(quadratic)")
GROUPBOX gb_pool 105 95 90 60, ///
label("Pooling")
RADIO r_fixed 110 110 80 ., ///
label("Fixed") first ///
option(NONE)
RADIO r_random @ 130 @ ., ///
label("Random") last ///
option("reffect")
CHECKBOX cb_eform 205 100 70 ., ///
label("Eform") ///
option("eform")
CHECKBOX cb_flip 205 120 70 ., ///
label("Flip") ///
option("flip")
CHECKBOX cb_mixed 205 140 70 ., ///
label("Mixed") ///
option("mixed")
CHECKBOX cb_graph 275 100 70 ., ///
label("Graph") ///
onclickon(program main_cb_graph_on) ///
onclickoff(program main_cb_graph_off) ///
option("graph")
CHECKBOX cb_funnel 275 120 70 ., ///
label("Funnel") ///
option("funnel")
CHECKBOX cb_print 275 140 70 ., ///
label("Print") ///
option("print") ///
onclickon(program main_cb_print_on) ///
onclickoff(program main_cb_print_off)
CHECKBOX cb_id 110 157 100 ., ///
label("ID Variable:") ///
onclickon(main.vn_id.enable) ///
onclickoff(main.vn_id.disable)
VARNAME vn_id 210 160 130 ., ///
label("ID Variable") ///
option("id")
CHECKBOX cb_fill_save 10 185 90 ., ///
label("Save filled:") ///
onclickon(main.fi_fill_save.enable) ///
onclickoff(main.fi_fill_save.disable)
FILE fi_fill_save 110 185 230 ., ///
save ///
label("File Name") ///
error("Save File Name") ///
dialogtitle("Save file") ///
filter("Stata Data|*.dta") ///
option("save")
CHECKBOX cb_gph_save 10 210 90 ., ///
label("Save gph:") ///
onclickon(main.fi_gph_save.enable) ///
onclickoff(main.fi_gph_save.disable)
FILE fi_gph_save 110 210 230 ., ///
save ///
label("File Name") ///
error("Saving File Name") ///
dialogtitle("Saving file") ///
filter("Stata Graphs|*.gph") ///
option("saving")
END
INCLUDE ifin
PROGRAM main_cb_graph_on
BEGIN
call main.cb_id.enable
if main.cb_id {
call main.vn_id.enable
}
END
PROGRAM main_cb_graph_off
BEGIN
if !main.cb_print {
call main.cb_id.disable
call main.vn_id.disable
}
END
PROGRAM main_cb_print_on
BEGIN
call main.cb_id.enable
if main.cb_id {
call main.vn_id.enable
}
END
PROGRAM main_cb_print_off
BEGIN
if !main.cb_graph {
call main.cb_id.disable
call main.vn_id.disable
}
END
SCRIPT main_se_on
BEGIN
main.tx_se.show
main.vl_se.show
main.tx_se.enable
main.vl_se.enable
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_var_on
BEGIN
main.tx_var.show
main.vl_var.show
main.tx_var.enable
main.vl_var.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_ci.disable
main.vl_ci.disable
main.tx_ci.hide
main.vl_ci.hide
END
SCRIPT main_ci_on
BEGIN
main.tx_ci.show
main.vl_ci.show
main.tx_ci.enable
main.vl_ci.enable
main.tx_se.disable
main.vl_se.disable
main.tx_se.hide
main.vl_se.hide
main.tx_var.disable
main.vl_var.disable
main.tx_var.hide
main.vl_var.hide
END
PROGRAM command
BEGIN
put "metatrim "
if main.r_se {
varlist main.vl_se
}
if main.r_var {
varlist main.vl_var
}
if main.r_ci {
varlist main.vl_ci
}
INCLUDE _ifin_pr
beginoptions
option radio(main r_se r_var r_ci)
option radio(main r_linear r_run r_quadratic)
option radio(main r_fixed r_random)
option main.cb_eform
option main.cb_flip
option main.cb_mixed
option main.cb_print
option main.cb_graph
option main.cb_funnel
optionarg main.vn_id
optionarg main.fi_fill_save
optionarg main.fi_gph_save
endoptions
END

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*! 1.4.5 TJS 24 May 2001
* based on hplot 1.4.4 NJC 23 May 2001
program define mhplot
version 6.0
#delimit ;
syntax varlist(numeric) [if] [in]
[, BOrder SOrt(string) noXaxis noYaxis PEn(string) Symbol(string)
T1title(string) T2title(string asis) TItle(str asis) TTIck VALLBL(varname)
XLAbel(numlist) XLIne(numlist) XSCale(str) XTIck(numlist) Axtol(int 600)
Blank flipt Format(string) FONTC(int 290) FONTR(int 570) FONTCB(int 444)
FONTRB(int 923) GAPMag(real 1) GAPs(numlist int) GLegend(string)
GLLJ GLPOS(int -1) t2m(int 0) Grid LAP Legend(string) LIne NIT2
t1m(int 0) PTSize(int 275) Range RLegend(varname) rllj RLPos(int 31500)
TIM(int 0) Vat VATFmt(string) VATPos(int 31500) Cstart(int -1)
SAving(string) PENText(int 1) ] ;
#delimit cr
qui {
tempvar touse order gleg gap dmin dmax dneg dpos z
mark `touse' `if' `in'
Markout2 `touse' `varlist'
gen `order' = _n
gsort - `touse' `sort' `order'
count if `touse'
local nuse = r(N)
* legend on left
if "`legend'" == "" {
tempvar legend
gen str1 `legend' = " "
if "`blank'" == "" { replace `legend' = string(_n) if `touse' }
}
else {
confirm variable `legend'
capture confirm string variable `legend'
if _rc == 7 {
tempvar legend2
capture decode `legend', gen(`legend2')
if _rc {
gen str1 `legend2' = ""
replace `legend2' = string(`legend')
}
else { replace `legend2' = string(`legend') if missing(`legend2') }
local legend "`legend2'"
}
}
local leglen : type `legend'
local leglen = substr("`leglen'", 4, .)
if `cstart' == -1 {
local cstart = 2000 + int(9000 * `leglen' / 25)
}
* legend on right
if "`rlegend'" != "" {
capture confirm string variable `rlegend'
if _rc == 7 {
tempvar rleg2
capture decode `rlegend', gen(`rleg2')
if _rc == 0 {
replace `rleg2' = string(`rlegend') if missing(`rleg2')
local rlegend "`rleg2'"
}
}
}
* axis scale
if "`xscale'" != "" {
tokenize "`xscale'", parse(",")
if "`4'" != "" | "`2'" != "," {
di in r "invalid xscale( ) option"
exit 198
}
local xscmin `1'
local xscmax `3'
}
if "`xscmin'" == "" { local min 0 }
else local min `xscmin'
if "`xscmax'" == "" { local max 0 }
else local max `xscmax'
* xlabel xtick xline might extend graph range beyond data range
if "`xlabel'`xtick'`xline'" != "" {
numlist "`xlabel' `xtick' `xline'", sort
local nn : word count `r(numlist)'
local xmin : word 1 of `r(numlist)'
local xmax : word `nn' of `r(numlist)'
local min = min(`min', `xmin')
local max = max(`max', `xmax')
}
* gap legend?
gen str1 `gleg' = " "
local glj = cond("`gllj'" != "", -1, 1)
if "`glegend'" != "" {
tokenize "`glegend'", parse("!")
local j 1
while "`1'" != "" {
if "`1'" != "!" {
if "`1'" == "." { local 1 " " }
local gleg`j' "`1'"
local j = `j' + 1
}
mac shift
}
}
* gaps between lines?
gen byte `gap' = 0
if "`gaps'" != "" {
local j 1
numlist "`gaps'", int range(>=0)
tokenize `r(numlist)'
while "`1'" != "" {
if "`1'" == "0" {
local gleg0 "`gleg`j''"
if "`gleg0'" == "" { local gleg0 " " }
}
else {
replace `gap' = 1 in `1' if `1' <= `nuse'
replace `gleg' = "`gleg`j''" in `1' if `1' <= `nuse'
}
local j = `j' + 1
mac shift
}
}
count if `gap'
local ngaps = r(N) + ("`gleg0'" != "")
* data range
tokenize `varlist'
local nvars : word count `varlist'
gen `dmin' = `1'
gen `dmax' = `1'
if `nvars' >= 2 {
local i 2
while `i' <= `nvars' {
replace `dmin' = min(`dmin', ``i'')
replace `dmax' = max(`dmax', ``i'')
local i = `i' + 1
}
}
su `dmin' if `touse', meanonly
local min = min(`min', r(min))
su `dmax' if `touse', meanonly
local max = max(`max', r(max))
local drange = `max' - `min'
local zero = cond(`min' >= 0, max(0, `min'), min(0, `max'))
gen `z' = `zero'
gen `dneg' = min(`dmin', `z')
gen `dpos' = max(`dmax', `z')
}
* start of parameter block
local t1start 1000 /* row for t1title */
local t2start 1900 /* row for t2title */
local ybeg 2400 /* start of y-axis */
local ylength 17600
* `axtol' is space at ends of y-axis
* axtol too large => ystep negative FW
if `axtol' > `ylength' / 2 {
di in bl "axtol too large: reset to default 600"
local axtol 600
}
* row where first line starts
local ystart = `ybeg' + `axtol'
* step between lines: one gap defaults to one line
local ystep = (`ylength' - 2 * `axtol') / (`nuse' - 1 + `ngaps' *`gapmag')
local yend = `ybeg' + `ylength'
local ynudge = 200 * (`fontr' / 570)^2
/* text displaced downwards from lines */
local ytick 400 /* tick length */
local yleg 1000 /* labels down from axis */
local yleg = `yend' + `yleg'
local ytitle 1400 /* title down from labels */
local ytitlef 900 /* title down from labels, flip titles */
local xstart `cstart' /* col where first line begins */
local xgap 400 /* gap between left legend and body of plot */
local xbeg = `xstart' - `xgap'
if `glpos' == -1 { local glpos `xbeg' }
local xlength = 30000 - `xstart'
/* horizontal extent of data region */
local xend = `xbeg' + `xgap' + `xlength'
local xz = `xbeg' + `xgap' + `xlength' * (`zero' - `min') / `drange'
local mcent = (`cstart' + 30000) / 2 + `tim'
/* col where main title centred */
local dotsp 150 /* spacing between dots */
if "`format'" == "" { local format "%1.0f" }
if "`vatfmt'" == "" { local vatfmt "%1.0f" }
local ahl = 500 * `ptsize' / 275 /* arrowhead length */
local aha = _pi / 6 /* arrowhead angle, between head and stem */
local barht `ahl' /* bar height */
if "`symbol'" == "" {
if `nvars' < 6 { local symbol "46253" }
else local symbol : di _dup(`nvars') "4"
}
else if length("`symbol'") == 1 & `nvars' > 1 {
local symbol : di _dup(`nvars') "`symbol'"
}
Gphtrans `symbol'
local symbol "`r(symbol)'"
if "`pen'" == "" { local pen : di _dup(`nvars') "2" }
else if length("`pen'") == 1 & `nvars' > 1 {
local pen : di _dup(`nvars') "`pen'"
}
* end of parameter block
* start gph
if "`saving'" != "" { local saving ", saving(`saving')" }
gph open `saving' /* FW */
gph pen `pentext'
gph font `fontr' `fontc'
* y-axis
if "`yaxis'" == "" { gph line `ybeg' `xstart' `yend' `xstart' }
* ttick => top ticks
local ttick = "`ttick'" == "ttick"
* ttick should => border FW
if `ttick' { local border "border" }
* x-axis and labels
if "`xaxis'" == "" {
gph line `yend' `xstart' `yend' `xend'
local ytick2 = `ybeg' - `ytick' / 2
local ytick = `yend' + `ytick'
if "`xlabel'" == "" {
gph line `yend' `xstart' `ytick' `xstart'
gph line `yend' `xend' `ytick' `xend'
if `ttick' {
gph line `ybeg' `xstart' `ytick2' `xstart'
gph line `ybeg' `xend' `ytick2' `xend' /* FW */
}
local text = cond("`lap'" == "lap", abs(`min'), `min')
local text : di `format' `text'
gph text `yleg' `xstart' 0 0 `text'
local text = cond("`lap'" == "lap", abs(`max'), `max')
local text : di `format' `text'
gph text `yleg' `xend' 0 0 `text'
}
else {
numlist "`xlabel'"
tokenize `r(numlist)'
if "`vallbl'" != "" { local vallbl : value label `vallbl' }
while "`1'" != "" {
local xtickp = `xbeg' + `xgap' + `xlength' * (`1' - `min') / `drange'
gph line `yend' `xtickp' `ytick' `xtickp'
if `ttick' { gph line `ybeg' `xtickp' `ytick2' `xtickp' }
local text = cond("`lap'" == "lap", abs(`1'), `1')
if "`vallbl'" != "" {
local label : label `vallbl' `text'
if "`label'" != "" { local text "`label'" }
}
else local text : di `format' `text'
gph text `yleg' `xtickp' 0 0 `text'
mac shift
}
}
}
* x-ticks
if "`xtick'" != "" {
numlist "`xtick'"
tokenize `r(numlist)'
while "`1'" != "" {
local xtickp = `xbeg' + `xgap' + `xlength' * (`1' - `min') / `drange'
gph line `yend' `xtickp' `ytick' `xtickp'
if `ttick' { gph line `ybeg' `xtickp' `ytick2' `xtickp' }
mac shift
}
}
* x-lines
if "`xline'" != "" {
numlist "`xline'"
tokenize `r(numlist)'
while "`1'" != "" {
local xli = `xbeg' + `xgap' + `xlength' * (`1' - `min') / `drange'
gph line `yend' `xli' `ybeg' `xli'
mac shift
}
}
* border
if "`border'" != "" {
gph line `ybeg' `xstart' `ybeg' `xend'
gph line `ybeg' `xend' `yend' `xend'
if "`xaxis'" != "" { gph line `yend' `xstart' `yend' `xend' }
}
* gap legend above first data point
if "`gleg0'" != "" {
local y2 = `ystart' + (`gapmag' - 1) * `ystep' + `ynudge'
gph text `y2' `glpos' 0 `glj' `gleg0'
}
* for each variable
tokenize `varlist'
local j 1
while "`1'" != "" {
local data "`1'"
local sy = substr("`symbol'", `j', 1)
local pe = substr("`pen'", `j', 1)
local y `ystart'
if "`gleg0'" != "" { local y = `y' + `ystep' * `gapmag' }
* for each observation
local i 1
while `i' <= `nuse' {
/* tjs code */
* dots and/or lines
if `j' == 1 {
local xmax = `xbeg' + `xgap' + /*
*/ `xlength' * (`dmax'[`i'] - `min') / `drange'
local xmin = `xbeg' + `xgap' + /*
*/ `xlength' * (`dmin'[`i'] - `min') / `drange'
if "`line'" == "line" { gph line `y' `xmin' `y' `xmax' }
if "`line'`grid'" == "" {
if "`range'" == "range" {
local ndots = int(abs(`xmax' - `xmin') / `dotsp')
tempvar ys xs
if `ndots' > _N { qui set obs `ndots' }
qui gen `ys' = `y'
qui gen `xs' = _n
qui replace `xs' = `xmin' + (`xs' - 1) * `dotsp'
qui replace `xs' = . if `xs' < `xmin' | `xs' > `xmax'
gph vpoint `ys' `xs', size(`ptsize') symbol(0)
}
else {
local ndots = int(abs(`xmax' - `xz') / `dotsp')
tempvar ys xs
if `ndots' > _N { qui set obs `ndots' }
qui gen `ys' = `y'
qui gen `xs' = _n
qui replace `xs' = `xz' + (`xs' - 1) * `dotsp' * sign(`dpos'[`i'])
qui replace `xs' = . if `xs' < `xmin' | `xs' > `xmax'
gph vpoint `ys' `xs', size(`ptsize') symbol(0)
local ndots = int(abs(`xmin' - `xz') / `dotsp')
tempvar ys xs
if `ndots' > _N { qui set obs `ndots' }
qui gen `ys' = `y'
qui gen `xs' = _n
qui replace `xs' = `xz' + (`xs' - 1) * `dotsp' * sign(`dneg'[`i'])
qui replace `xs' = . if `xs' < `xmin' | `xs' > `xmax'
gph vpoint `ys' `xs', size(`ptsize') symbol(0)
}
}
if "`grid'" != "" {
tempvar ys xs
local ndots = int(abs(`xstart' - `xend') / `dotsp')
if `ndots' > _N { qui set obs `ndots' }
qui gen `ys' = `y'
qui gen `xs' = _n
qui replace `xs' = `xstart' + (`xs' - 1) * `dotsp'
qui replace `xs' = . if `xs' > `xend'
gph vpoint `ys' `xs', size(`ptsize') symbol(0)
}
}
/* tjs code end */
* data point
gph pen `pe'
local x = `xbeg' + `xgap' + `xlength' * (`data'[`i'] - `min') / `drange'
if `data'[`i'] < . & "`sy'" != "i" {
if "`sy'" == "a" {
if `j' == 1 { local sign 1 }
else local sign = sign(`data'[`i'] - `prev'[`i'])
Gphhah `y' `x' `sign' `ahl' `aha'
}
else if "`sy'" == ">" { Gphhah `y' `x' 1 `ahl' `aha' }
else if "`sy'" == "<" { Gphhah `y' `x' -1 `ahl' `aha' }
else Gphpt `sy' `y' `x' `barht' `ptsize'
}
* text
gph pen `pentext'
local y2 = `y' + `ynudge'
if "`vat'" != "" & `nvars' == 1 {
local text : di `vatfmt' `data'[`i']
gph text `y2' `vatpos' 0 1 `text'
}
if `j' == 1 {
local text = `legend'[`i']
gph text `y2' `xbeg' 0 1 `text'
}
if `j' == 1 & "`rlegend'" != "" {
local text = `rlegend'[`i']
local rlj = cond("`rllj'" != "", -1, 1)
gph text `y2' `rlpos' 0 `rlj' `text'
}
* gap
if `gap'[`i'] {
local y = `y' + `ystep' * `gapmag'
if `j' == 1 {
local text = `gleg'[`i']
local y2 = `y' + `ynudge'
gph text `y2' `glpos' 0 `glj' `text'
}
}
local y = `y' + `ystep'
local i = `i' + 1
}
* next observation
local prev `1'
local j = `j' + 1
mac shift
}
* next variable
* t2title, left justified (defaults to key for 2 or more variables)
if "`t2title'" != "" & trim("`t2title'") == "" { local t2title }
else if "`t2title'" != "" { gph text `t2start' `xstart' 0 -1 `t2title' }
else if `nvars' >= 2 {
local t2 = `t2start' - `ynudge'
local xjump = `xlength' / `nvars'
local xjump2 = `xjump' / 50
local x = `xstart' + `xjump2' + `t2m'
local j 1
while `j' <= `nvars' {
local sy = substr("`symbol'", `j', 1)
local pe = substr("`pen'", `j', 1)
gph pen `pe'
if "`sy'" == "a" {
local ahv = index("`symbol'", "a")
if `ahv' > 1 {
local this : word `ahv' of `varlist'
local ahvm1 = `ahv' - 1
local prev : word `ahvm1' of `varlist'
count if `this' >= `prev' & `touse'
local majsign = cond(r(N) > `nuse' / 2, 1, -1)
}
else local majsign 1
local x3 = `x' + 0.6 * `ptsize' * `majsign'
Gphhah `t2' `x3' `majsign' `ahl' `aha'
}
else if "`sy'" == ">" {
local x3 = `x' + 0.6 * `ptsize'
Gphhah `t2' `x3' 1 `ahl' `aha'
}
else if "`sy'" == "<" {
local x3 = `x' - 0.6 * `ptsize'
Gphhah `t2' `x3' -1 `ahl' `aha'
}
else if "`sy'" != "i" {
Gphpt `sy' `t2' `x' `barht' `ptsize'
}
local x2 = `x' + `xjump2'
local var : word `j' of `varlist'
if "`nit2'" == "" {
local text : variable label `var'
if "`text'" == "" { local text "`var'" }
}
else local text "`var'"
gph pen `pentext'
gph text `t2start' `x2' 0 -1 `text'
local x = `x' + `xjump'
local j = `j' + 1
}
}
* title and t1title
if `"`title'"' == `""' & `nvars' == 1 {
local title : variable label `data'
if "`title'" == "" { local title "`data'" }
}
else if `"`title'"' != `""' { local title `title' }
local xL = `xstart' + `t1m'
if "`flipt'" == "" { /* default */
* t1title, left justified
gph text `t1start' `xL' 0 -1 `t1title'
* main title at bottom, centred
gph font `fontrb' `fontcb'
local ytitle = `yleg' + `ytitle'
gph text `ytitle' `mcent' 0 0 `title'
}
else { /* flip titles from default */
* bottom title, centred (and closer to axis than default)
local ytitle = `yleg' + `ytitlef'
gph text `ytitle' `mcent' 0 0 `t1title'
* main title at top, left justified
gph font `fontrb' `fontcb'
gph text `t1start' `xL' 0 -1 `title'
}
gph close
end
program define Gphhah /* horizontal arrow head */
* `1' y position of tip
* `2' x position of tip
* `3' sign determines direction: 1 = >, -1 = <
* `4' arrowhead length
* `5' arrowhead interior angle (between shaft and head)
* 1.0.1 NJC 18 February 1999
* 1.0.0 NJC 27 May 1997
version 6.0
local ah1y = `1' - `3' * `4' * sin(`5')
local ah1x = `2' - `3' * `4' * cos(`5')
local ah2y = `1' + `3' * `4' * sin(`5')
local ah2x = `2' - `3' * `4' * cos(`5')
gph line `ah1y' `ah1x' `1' `2'
gph line `ah2y' `ah2x' `1' `2'
end
program define Gphbar /* vertical bar */
* Gphbar yposition xposition bar_height
* 1.0.1 NJC 18 February 1999
* 1.0.0 NJC 27 May 1997
version 6.0
local by1 = `1' - 0.5 * `3'
local by2 = `1' + 0.5 * `3'
gph line `by1' `2' `by2' `2'
end
program define Gphcross /* cross X */ /* FW */
* Gphcross yposition xposition bar_height
* 1.0.1 NJC 18 February 1999
* 1.0.0 NJC 6 October 1997
version 6.0
local Xy1 = `1' - 0.5 * `3'
local Xy2 = `1' + 0.5 * `3'
local Xx1 = `2' - 0.5 * `3'
local Xx2 = `2' + 0.5 * `3'
gph line `Xy1' `Xx1' `Xy2' `Xx2'
gph line `Xy2' `Xx1' `Xy1' `Xx2'
end
program def Markout2 /* marks out obs with all missing values */
* 1.0.2 NJC 16 February 1999
* 1.0.1 NJC 25 March 1998
version 6.0
syntax varlist(min=1) [, Strok ]
tokenize `varlist'
local nvars : word count `varlist'
if `nvars' == 1 { exit 0 }
local nvars = `nvars' - 1
local markvar `1'
mac shift
tempvar nmiss
gen `nmiss' = 0
qui {
while "`1'" != "" {
local type : type `1'
if substr("`type'", 1, 3) == "str" {
if "`strok'" != "" { replace `nmiss' = `nmiss' + (`1' == "") }
else replace `nmiss' = `nmiss' + 1
}
else replace `nmiss' = `nmiss' + (`1' == .)
mac shift
}
replace `nmiss' = `nmiss' == `nvars'
replace `markvar' = 0 if `nmiss'
}
end
program def Gphtrans, rclass /* transliterate ".OSTodp" -> "0123456" */
* 1.0.2 NJC 1 March 1999
* 1.0.0 NJC 31 March 1998
version 6.0
args argin
local length = length("`argin'")
local i 1
while `i' <= `length' {
local s = substr("`argin'", `i', 1)
if "`s'" == "." { local s 0 }
else if "`s'" == "O" { local s 1 }
else if "`s'" == "S" { local s 2 }
else if "`s'" == "T" { local s 3 }
else if "`s'" == "o" { local s 4 }
else if "`s'" == "d" { local s 5 }
else if "`s'" == "p" { local s 6 }
local argout "`argout'`s'"
local i = `i' + 1
}
return local symbol `argout'
end
program def Gphpt
* 1.2.2 NJC 24 February 1999 smaller big cross, bigger ,
* 1.2.1 NJC 18 February 1999
* 1.2.0 NJC 12 Jan 1999
* 1.1.0 NJC 24 Sept 1998
* 1.0.0 NJC 6 April 1998
version 6.0
args sy y x barht ptsize
if "`sy'" == "|" { Gphbar `y' `x' `barht' }
else if "`sy'" == "," { Gphbar `y' `x' 0.6*`barht' }
else if "`sy'" == "X" { Gphcross `y' `x' 0.707*`barht' }
else if "`sy'" == "x" { Gphcross `y' `x' 0.5*`barht' }
else if "`sy'" == "-" { Gphhbar `y' `x' 0.5*`barht' }
else gph point `y' `x' `ptsize' `sy'
end
program define Gphhbar /* horizontal bar */
* Gphhbar yposition xposition bar_length
* 1.0.1 NJC 18 February 1999
* 1.0.0 NJC 24 Sept 1998
version 6.0
local bx1 = `2' - 0.5 * `3'
local bx2 = `2' + 0.5 * `3'
gph line `1' `bx1' `1' `bx2'
end

437
Modules/ado/plus/m/micombine.ado Normal file
View File

@ -0,0 +1,437 @@
*! version 1.1.1 PR 28nov2005
/*
History
1.1.1 28nov2005 eform and eform() option styles now allowed.
nowarning option to suppress warning message about supported regression cmds.
1.1.0 30sep2005 Generalization of supported commands (cmdchk.ado also modified).
Version <=7 no longer supported.
1.0.9 18apr2005 Default rowid variable becomes _i.
If not found, take from char _dta[MI_obsid] then char _dta[mi_id].
Default impid variable is _j. If not found, take from _dta[MI_impid].
These changes are for future compatibility with MItools.
1.0.8 04mar2005 Fixed problem with eform on redisplay
1.0.7 28jan2005 Fixed problem with null model estimation
Fixed bug with noconstant option
1.0.6 25jan2005 e(sample) now correct for all imputations, via ereturn post.
Add d.f. quantities from Barnard & Rubin (1999) Biometrika 86:948-955 eq (3)-(5).
Minor additions to ereturn quantities for compatibility with misw.
Allow null model and model only with constant for benefit of misw.
1.0.5 16nov2004 Update calculations of quantities stored for Li et al (1991) F test.
1.0.4 13oct2004 Change e(cmd) to `cmd', make e(cmd2)="micombine" (for use after mfx).
Implementation of mean log likelihood and chisquare statistic saved
(note undocumented -noclear- but v. useful option to ereturn post/estimates post).
*/
program define micombine, eclass
if _caller()<=7 {
di as error "version 7 and earlier not supported"
exit 9
}
if replay() {
if `"`e(cmd2)'"'!="micombine" {
error 301
}
syntax[, EForm EForm2(string)]
if "`eform2'"!="" {
if "`eform'"!="" di as err "[eform ignored]"
local eform eform(`eform2')
}
else if "`eform'"!="" local eform eform("exp(b)")
di as text _n "Multiple imputation parameter estimates (" as res e(m) as text " imputations)"
capture ereturn display, `eform'
local rc=_rc
if `rc'>0 {
* Null model, or model with cc() only
if `"`e(cmd)'"'!="stcox" {
`e(cmd)' `0'
}
else di as text "[Null model - no estimates]"
}
else ereturn display, `eform'
di as result e(N) as text " observations."
exit
}
gettoken cmd 0 : 0
if "`cmd'"=="stpm" {
local dist 7
local bad 0
}
else {
cmdchk `cmd'
local cmdnotknown `s(bad)'
/*
dist=0 (normal), 1 (binomial), 2 (poisson), 3 (cox), 4 (glm),
5 (xtgee), 6(ereg/weibull).
*/
local dist `s(dist)'
}
syntax [anything] [if] [in] [aw fw pw iw] , [ IMPid(string) BR CC(varlist) noCONStant ///
DEAD(varname) DETail EForm EForm2(string) GENxb(string) LRR noWARning OBSid(string) * ]
if `cmdnotknown' & "`warning'"!="nowarning" {
di as err _n "Warning: " as inp "`cmd'" as err " is not a certified regression command for micombine."
di as err "micombine will continue mechanically, but correct results are not guaranteed."
di as err "You must take responsibility that Rubin's rules are appropriate here." _n
}
if "`eform2'"!="" {
if "`eform'"!="" di as err "[eform ignored]"
local eform eform(`eform2')
}
else if "`eform'"!="" local eform eform("exp(b)")
* Check for possible impid characteristic. If it does not exist use default _j
if "`impid'"=="" {
local impid: char _dta[MI_impid]
if "`impid'"=="" local impid _j
}
cap confirm var `impid'
if _rc {
di as err "imputation identifier `impid' not found"
exit 601
}
* Check for possible obsid chars. If none exist use default _i
if "`obsid'"=="" {
local I: char _dta[MI_obsid]
if "`I'"=="" local I: char _dta[mi_id]
if "`I'"=="" local I _i
}
else local I `obsid'
cap confirm var `I'
if _rc {
di as err "observation identifier `I' not found"
exit 601
}
if "`detail'"!="" {
local detail noisily
}
else local detail
*Change here 11/15/05, commenting out the line below !! PR DELETED XIAO CHEN'S EDIT, NOV 05
frac_cox "`dead'" `dist'
if "`constant'"=="noconstant" {
if "`cmd'"=="fit" | "`cmd'"=="stcox" | "`cmd'"=="cox" {
di as error "noconstant invalid with `cmd'"
exit 198
}
}
*Change here 11/15/05, `dist' could be null.... !! PR DELETED XIAO CHEN'S EDIT, NOV 05
*if "`dist'" =="7" { /* stcox, streg, stpm */
if `dist'==7 {
local y
local yname _t
local xvars `anything'
}
else {
gettoken y xvars : anything
gettoken xvars left: xvars, parse("(")
local yname `y'
}
tempvar touse
quietly {
marksample touse
markout `touse' `varlist' `dead' `cc'
if "`dead'"!="" {
local dead "dead(`dead')"
}
* Deal with weights.
frac_wgt `"`exp'"' `touse' `"`weight'"'
local wgt `r(wgt)'
tempvar J
* Important (compatibility with mitools): ignore rows for which impid=0
egen int `J'=group(`impid') if `touse'==1 & `impid'>0 & !missing(`impid')
sum `J', meanonly
local m=r(max)
if `m'<2 {
di as error "there must be at least 2 imputations"
exit 198
}
local nxvar: word count `xvars'
/*
if `nxvar'<1 {
di as err "there must be at least one covariate"
exit 198
}
*/
local ncc: word count `cc' /* could legitimately be zero */
local nvar=`nxvar'+`ncc' /* number of covariates in model */
count if `touse'==1 & `J'==1
local nobs=r(N)
* Null Cox model (or model with only ccvars): fit on final imputation only, and quit
if "`xvars'"=="" & ("`cmd'"=="cox" | "`cmd'"=="stcox") {
if "`cmd'"=="stcox" & "`cc'"=="" {
local options `options' estimate
}
`detail' `cmd' `y' `cc' if `touse'==1 & `J'==`m' `wgt', `options' `dead' `constant'
noi `cmd'
di as result `nobs' as text " observations."
ereturn scalar m=`m'
ereturn local impid `impid'
ereturn local cmd `cmd'
ereturn local cmd2 micombine
exit
}
* Compute model over m imputations
tempname W Q B T QQ
if "`genxb'"!="" {
tempvar xb xbtmp
gen `xb'=.
}
* Estimate mean LR chisquare statistic (where possible)
tempname chi2 ell ell0 nucom
scalar `chi2'=0
scalar `ell'=0
scalar `ell0'=0
forvalues i=1/`m' {
tempname Q`i'
`detail' `cmd' `y' `xvars' `cc' `left' if `touse'==1 & `J'==`i' `wgt', ///
`options' `dead' `constant'
scalar `nucom'=e(df_r) // complete-data residual degrees of freedom
if `nucom'==. {
scalar `nucom'=100000
}
scalar `ell'=`ell'+e(ll)
scalar `ell0'=`ell0'+e(ll_0)
scalar `chi2'=`chi2'-2*(e(ll_0)-e(ll))
if "`genxb'"!="" {
predict `xbtmp' if `touse'==1 & `J'==`i', xb
replace `xb'=`xbtmp' if `touse'==1 & `J'==`i'
drop `xbtmp'
}
matrix `Q`i''=e(b)
if `i'==1 {
matrix `Q'=e(b)
matrix `W'=e(V)
}
else {
matrix `Q'=`Q'+e(b)
matrix `W'=`W'+e(V)
}
}
if "`genxb'"!="" {
sort `touse' `I' `J'
by `touse' `I': gen `genxb'=sum(`xb')/`m' if `touse'==1
by `touse' `I': replace `genxb'=`genxb'[_N] if _n<_N
lab var `genxb' "Mean Linear Predictor (`m' imputations)"
}
matrix `Q'=`Q'/`m' /* MI param estimates */
matrix `W'=`W'/`m'
scalar `chi2'=`chi2'/`m'
scalar `ell'=`ell'/`m'
scalar `ell0'=`ell0'/`m'
local k=colsof(`Q')
matrix `B'=J(`k',`k',0)
forvalues i=1/`m' {
matrix `QQ'=`Q`i''-`Q'
if `i'==1 {
matrix `B'=`QQ''*`QQ'
}
else matrix `B'=`B'+`QQ''*`QQ'
}
matrix `B'=`B'/(`m'-1)
matrix `T'=`W'+(1+1/`m')*`B' /* estimated VCE matrix */
/*
Relative increase in variance due to missing information (r) for
each variable, and df and lambda, the fraction of missing information.
All measures are unstable for low m. See Schafer (1997) p. 110.
Note that BIF = sqrt(T/W) = sqrt(1 + (B/W)*(1+1/m)) = sqrt(1+r)
is the between-imputation imprecision factor, i.e. the ratio
of the SE derived from T to the SE derived from W,
ignoring between-imputation variation in parameter estimates.
*/
tempname r t lambda nu BIF
matrix `r'=J(1,`k',0)
matrix `lambda'=J(1,`k',0)
matrix `nu'=J(1,`k',0)
matrix `BIF'=J(1,`k',0)
scalar `t'=`m'-1
* Next few lines assign quantities for tests of individual (1 df) components of Q (=beta)
forvalues j=1/`k' {
matrix `r'[1,`j']=(1+1/`m')*`B'[`j',`j']/`W'[`j',`j']
matrix `nu'[1,`j']=cond(`t'>4, 4+(`t'-4)*(1+(1-2/`t')/`r'[1,`j'])^2, `t'*(1+1/`r'[1,`j'])^2)
matrix `lambda'[1,`j']=(`r'[1,`j']+2/(`nu'[1,`j']+3))/(`r'[1,`j']+1)
matrix `BIF'[1,`j']=sqrt(1+`r'[1,`j']) /* = sqrt(`T'[`j',`j']/`W'[`j',`j']) */
}
* Next few lines assign quantities for d.f. from Barnard & Rubin 1999 B'ka 86(4): 948-955.
tempname nutilde num nuobs gamma
matrix `nutilde'=J(1,`k',0)
matrix `num'=J(1,`k',0)
matrix `nuobs'=J(1,`k',0)
matrix `gamma'=J(1,`k',0)
forvalues j=1/`k' {
matrix `gamma'[1,`j']=(1+1/`m')*`B'[`j',`j']/`T'[`j',`j']
matrix `nuobs'[1,`j']=((`nucom'+1)/(`nucom'+3))*`nucom'*(1-`gamma'[1,`j'])
matrix `num'[1,`j']=(`m'-1)*`gamma'[1,`j']^-2
matrix `nutilde'[1,`j']=1/((1/`num'[1,`j']+1/`nuobs'[1,`j']))
}
* use all varnames
local names: colnames(`Q1')
matrix colnames `r'=`names'
matrix colnames `nu'=`names'
matrix colnames `lambda'=`names'
matrix colnames `BIF'=`names'
matrix colnames `gamma'=`names'
matrix colnames `nuobs'=`names'
matrix colnames `num'=`names'
matrix colnames `nutilde'=`names'
* Li, Raghunathan & Rubin (1991) estimates of T and nu1
* for F test of Q=0 on k,nu1 degrees of freedom
tempname r1 t1 BW TLRR
matrix `BW'=`B'*syminv(`W')
scalar `r1'=trace(`BW')*(1+1/`m')/`k'
matrix `TLRR'=`W'*(1+`r1')
scalar `t1'=`k'*(`m'-1)
matrix colnames `Q'=`names'
matrix rownames `T'=`names'
matrix colnames `T'=`names'
matrix rownames `B'=`names'
matrix colnames `B'=`names'
matrix rownames `TLRR'=`names'
matrix colnames `TLRR'=`names'
}
di as text _n "Multiple imputation parameter estimates (`m' imputations)"
if "`lrr'"!="" {
di as text "[Using Li-Raghunathan-Rubin (LRR) estimate of VCE matrix]"
ereturn post `Q' `TLRR', depname(`yname') obs(`nobs') esample(`touse') noclear
ereturn matrix T `T'
}
else {
ereturn post `Q' `T', depname(`yname') obs(`nobs') esample(`touse') noclear
ereturn matrix TLRR `TLRR'
}
if "`br'"=="" {
ereturn display, `eform'
di as result `nobs' as text " observations."
}
ereturn matrix B `B'
ereturn matrix W `W'
ereturn matrix r `r'
ereturn matrix nu `nu'
ereturn matrix lambda `lambda'
ereturn matrix BIF `BIF'
* Quantities for calculating df `nutilde' according to Barnard & Rubin (1999)
ereturn matrix gamma `gamma'
ereturn matrix nuobs `nuobs'
ereturn matrix num `num'
ereturn matrix nutilde `nutilde'
ereturn scalar r1=`r1'
ereturn scalar nu1=cond(`t1'>4, 4+(`t1'-4)*(1+(1-2/`t1')/`r1')^2, 0.5*`t1'*(1+1/`k')*(1+1/`r1')^2)
ereturn scalar m=`m'
ereturn scalar chi2=`chi2'
ereturn scalar ll=`ell'
ereturn scalar ll_0=`ell0'
ereturn local eform `eform'
ereturn local impid `impid'
ereturn local cmd `cmd'
ereturn local cmd2 micombine
if "`br'"!="" {
display_t
di as result `nobs' as text " observations."
}
end
program define display_t
* Display results with t-statistics estimated according to Barnard & Rubin (1999)
tempname V Q nu
matrix `V'=e(V)
matrix `Q'=e(b)
matrix `nu'=e(nutilde)
local yname `e(depvar)'
local xs: colnames `Q'
local k=colsof(`Q')
di as text _n "Intervals and inference based on d.f. from Barnard & Rubin (1999)"
di as txt "{hline 13}{c TT}{hline 64}"
local t0 = abbrev("`yname'",12)
if `"`e(eform)'"'!="" {
local tt "Odds Ratio"
}
else {
local tt " Coef."
}
#delimit ;
di as text
%12s "`t0'" _col(14)"{c |}`tt' Std. Err. t P>|t| [$S_level% Conf. Intvl] MI.df"
_n "{hline 13}{c +}{hline 64}" ;
#delimit cr
tempname df mn se t p invt l u
forvalues i=1/`k' {
local x: word `i' of `xs'
if "`x'"!="_cons" {
local fmt : format `x'
if substr("`fmt'",-1,1)=="f" {
local fmt="%8."+substr("`fmt'",-2,2)
}
else if substr("`fmt'",-2,2)=="fc" {
local fmt="%8."+substr("`fmt'",-3,3)
}
else local fmt "%8.0g"
local fmt`i' `fmt'
}
else local fmt "%8.0g"
scalar `df' =`nu'[1,`i']
scalar `mn' = `Q'[1,`i']
scalar `se' = sqrt(`V'[`i',`i'])
scalar `t' = `mn'/`se'
scalar `p' = 2* ttail(`df', abs(`t'))
scalar `invt' = invttail(`df', (1-$S_level/100)/2)
scalar `l' = `mn' - `invt'*`se'
scalar `u' = `mn' + `invt'*`se'
if `"`e(eform)'"'!="" {
scalar `mn' = exp(`mn')
scalar `se' = `mn'*`se'
scalar `l' = exp(`l')
scalar `u' = exp(`u')
}
if `df'>99999 {
local fmtdf %9.2e
}
else local fmtdf %9.2f
di as text /*
*/ %12s abbrev("`x'",12) _col(14) "{c |}" /*
*/ _col(17) as res `fmt' `mn' /*
*/ _col(27) `fmt' `se' /*
*/ _col(36) %7.2f `t' /*
*/ _col(42) %7.3f `p' /*
*/ _col(52) `fmt' `l' /*
*/ _col(61) `fmt' `u' /*
*/ _col(70) `fmtdf' `df'
}
di as text "{hline 13}{c BT}{hline 64}"
end
program define chkrowid, sclass
local I: char _dta[mi_id]
if "`I'"=="" {
di as error "no row-identifier variable found - data may have incorrect format"
exit 198
}
cap confirm var `I'
local rc=_rc
if `rc' {
di as error "row-identifier variable `I' not found"
exit `rc'
}
sret local I `I'
end

284
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{smcl}
{* 28nov2005}{...}
{hline}
help for {hi:micombine}{right:(SJ5-4: st0067_2; SJ5-2: st0067_1; SJ4-3: st0067)}
{hline}
{title:Estimation of regression models with multiply imputed samples}
{p 8 18 2}
{cmd:micombine}
{{it:supported_regression_cmd} | {it:other_regression_cmd}}
[{it:yvar}]
[{it:covarlist}]
[{it:other_stuff]}
{ifin}
{weight}
[{cmd:,}
{cmd:br}
{cmdab:nocons:tant}
{cmdab:det:ail}
{cmdab:ef:orm}[{cmd:(}{it:string}{cmd:)}]
{cmdab:g:enxb(}{it:newvarname}{cmd:)}
{cmdab:imp:id(}{it:varname}{cmd:)}
{cmd:lrr}
{cmdab:nowar:ning}
{cmdab:obs:id(}{it:varname}{cmd:)}
{it:regression_cmd_options}]
{p 4 4 2}
where
{p 8 8 2}
{it:supported_regression_cmd}s are
{helpb clogit},
{helpb cnreg},
{helpb glm},
{helpb logistic},
{helpb logit},
{helpb mlogit},
{helpb ologit},
{helpb oprobit},
{helpb poisson},
{helpb probit},
{helpb qreg},
{helpb regress},
{helpb rreg},
{helpb stcox},
{helpb streg},
or
{helpb xtgee}, and {it:other_regression_cmd} is any other Stata regression command
(see Remarks).
{p 4 4 2}
{cmd:micombine} shares a subset of the features of all {help estcom:estimation commands};
see {it:Remarks}.
{p 4 4 2}
All weight types supported by {it:regression_cmd} are allowed; see
{help weight}.
{title:Description}
{p 4 4 2}
{cmd:micombine} estimates the parameters of a regression model whose
type is determined by {it:supported_regression_cmd} or {it:other_regression_cmd}.
Parameter estimates are combined
across several replicates obtained previously by multiple imputation,
e.g. by using {helpb ice} to create a file of imputed data.
See {it:Remarks} for a brief account of how {cmd:micombine} combines
the estimates and obtains standard errors.
{title:Options}
{p 4 8 2}
{cmd:br} calculates degrees of freedom and tests of significance for each predictor
according to the formulae (3)-(5) of Barnard & Rubin (1999).
After estimation, the required degrees of freedom are stored in a matrix
(column vector) {cmd:e(nutilde)}. Note that if {cmd:test}
is used after {cmd:micombine} for significance testing of regression
coefficients, such tests assume that the degrees of freedom are
equal to the number of observations minus the number of parameters
estimated, not those given in {cmd:e(nutilde)}.
{p 4 8 2}
{cmd:noconstant} suppresses the regression constant in all regressions.
{p 4 8 2}
{cmd:detail} gives details of the regression model for each imputation.
{p 4 8 2}
{cmd:eform}[{cmd:(}{it:string}{cmd:)}] specifies that the exponentiated form
of the coefficients be output and that the constant not be reported.
The exponentiated coefficients are labeled {cmd:exp(b)}, unless
the optional {it:string} is used.
{p 4 8 2}
{cmd:genxb(}{it:newvarname}{cmd:)} creates {it:newvarname} to hold the
linear predictor from each regression model, averaged over all the
imputations.
{p 4 8 2}
{cmd:impid(}{it:varname}{cmd:)} specifies that {it:varname} is the variable
identifying the imputations. The number of imputations is determined as
the number of unique values of {it:varname}. All observations for which
{it:varname} takes the value zero are ignored in the analysis.
The default {it:varname} is {cmd:_j}.
{p 4 8 2}
{cmd:lrr} specifies that the Li-Raghunathan-Rubin (LRR) robust estimate of the
variance-covariance matrix of the regression coefficients be used.
{p 4 8 2}
{cmd:nowarning} suppresses the warning message about the use
of {it:other_regression_cmd}s (see {it:Remarks}).
{p 4 8 2}
{cmd:obsid(}{it:varname}{cmd:)} is provided to allow {cmd:micombine} to analyze
datasets created by programs other than {cmd:ice}. {it:varname} specifies the name
of a variable holding the "observation ID", i.e. the sequence number of each
observation in a given imputation. The number of observations should
be identical between imputations, as should the order of the observations.
{it:varname} should run 1,...,N for imputation 1, 1,...,N for imputation 2, and
so on. {cmd:ice} automatically stores the information with the data, so this
option is not required. The default {it:varname} is {cmd:_i}.
{p 4 8 2}
{it:regression_cmd_options} may be any of the options appropriate to
{it:regression_cmd}.
{title:Remarks}
{p 4 4 2}
Details of statistical inference from multiple imputed datasets are nicely
described in a recent Stata Journal article by John Carlin and colleagues
(Carlin et al. 2003). Here, with due acknowledgment to John, I give an edited
version of section 2 of his article.
{p 4 4 2}
A simple method of combining estimates from several models was derived by
Rubin (1987). Suppose initially that primary interest lies in estimating a
scalar quantity, Q. Here, Q is a regression coefficient, for example, the log
hazard ratio in a proportional hazards model. Suppose that we have imputed m
complete datasets using an appropriate model. In each dataset, standard
complete-data methods are used to obtain an estimate of Q with an associated
standard error. Let Q(k) and U(k) denote the point estimate and variance
respectively from the kth (k = 1, 2, ... , m) dataset. The point estimate Q^
of Q from multiple imputation is simply the arithmetic mean of Q(1),...,Q(k).
{p 4 4 2}
Obtaining a valid standard error for this estimate of Q^ requires combining
information on within-imputation and between-imputation variation. The latter
is important in reflecting uncertainty due to variability between imputation
samples. First, a within-imputation variance component, W, is obtained as the
mean of the complete-data variance estimates, Q(1),....,Q(k). Second, a
between-imputation variance component, B, is calculated as the sum of squares
of Q(1),....,Q(k) about Q^, divided by m-1. The (total) variance T of Q^ is
given by
{p 8 12 2}
T = W + B * (1 + 1/m)
{p 4 4 2}
Rubin (1987) showed that (Q - Q^)/sqrt(T) is distributed approximately
as Student's t on nu degrees of freedom, where
{p 8 12 2}
nu = (m - 1) * (1 + W /(B * (1 + 1/m)))^2
{p 4 4 2}
The (1 + 1/m) term in these expressions indicates that it is not necessary to
a create large number of imputed datasets, particularly when B is much smaller
than W. The condition will be satisfied unless there is much missing data and
the parameter estimates within each dataset are very precise.
{title:Available regression commands}
{p 4 4 2}
{cmd:micombine} has been tested with the commands listed under
{it:supported_regression_cmd} at the beginning of this help file.
{cmd:micombine} {it:may} work satisfactorily with {it:other_regression_cmd}s,
but this cannot be guaranteed. This facility is provided so that the
researcher familiar with a particular Stata command has a fighting chance of
obtaining correct MI estimates and standard errors. HOWEVER, THE AUTHOR
DISCLAIMS ALL RESPONSIBILITY FOR THE CORRECTNESS OF RESULTS ARISING FROM USE
OF AN {it:other_regression_cmd}. Note that {it:other_stuff} in the syntax
diagram is code that may be required by some {it:other_regression_cmd}s, for
example {cmd:ivreg} wants {cmd:(}{it:varlist2}{cmd: = }{it:varlist_iv}{cmd:)}.
{cmd:micombine} parses for the occurrence of an opening parenthesis. There may
be other syntaxes that are not accommodated by this approach; if so, please
contact the author with details.
{title:Postestimation prediction}
{p 4 4 2}
The {cmd:predict} command {it:may} work as you expect after {cmd:micombine},
but this feature should be regarded as under development and should be
treated with caution. {cmd:micombine} stores the quantities needed by
{cmd:predict} at the last execution of the regression command, that is at the
final imputation, but prediction following some regression commands has
non-standard features that are hard to emulate accurately.
Known issues are as follows:
{p 8 12 2}
1. After {cmd:micombine mlogit}: {cmd:predict} may require that the outcome
levels are known as 0, 1, 2, ... , so it may be necessary to drop the
score label for the outcome variable, if such a label is defined.
This is KNOWN to be a problem using {cmd:mfx} following {cmd:micombine mlogit}.
For example, {cmd:mfx compute, predict(outcome(0))} will work only if
the lowest level of the outcome is 0, and is not labeled.
{p 8 12 2}
2. After {cmd:micombine} with a restricted sample (i.e. using {cmd:if},
{cmd:in} or zero weights for some observations, or some members
of {it:covarlist} still have missing values), the system variable
{cmd:e(sample)} is defined as you would expect it to be
only for the final imputation. In all earlier imputations it
is zero. Although not necessarily convenient for use of
{cmd:e(sample)} in data analysis, the behavior is correct for the
purposes of {cmd:predict}, since the relevant sample size and
estimation sample are properties of (any) one imputation,
but not of the complete assembly of imputations.
{title:Examples}
{p 4 8 2}{cmd:. ice y x1 x2 x3 using imp, m(10) genmiss(m_)}{p_end}
{p 4 8 2}{cmd:. use imp, clear}{p_end}
{p 4 8 2}{cmd:. micombine regress y x1 x2 x3}{p_end}
{p 4 8 2}{cmd:. stset time, failure(cens)}{p_end}
{p 4 8 2}{cmd:. micombine stcox x1 x2 x3, genxb(index)}{p_end}
{p 4 8 2}{cmd:. test x2==1}{p_end}
{p 4 8 2}{cmd:. testparm x1 x2}{p_end}
{title:Author}
{p 4 4 2}
Patrick Royston, MRC Clinical Trials Unit, London.
patrick.royston@ctu.mrc.ac.uk
{title:References}
{p 4 8 2}
Barnard, J. and D. B. Rubin. 1999. Small-sample degrees of freedom with
multiple imputation. {it:Biometrika} 86: 948-955.
{p 4 8 2}
Carlin, J. B., N. Li, P. Greenwood, and C. Coffey. 2003.
Tools for analyzing multiple imputed datasets. {it:Stata Journal} 3(3): 226-244.
{p 4 8 2}
Carlin, J. B., N. Li, P. Greenwood, and C. Coffey. 2003.
Tools for analyzing multiple imputed datasets. {it:Stata Journal} 3(3): 226-244.
{p 4 8 2}
Li, K., T. Raghunathan, and D. Rubin. 1991. Large sample significance levels
from multiply-imputed data using moment-based statistics and an F reference
distribution. {it:Journal of the American Statistical Association} 86: 1065-1073.
{p 4 8 2}
Rubin, D. 1987. {it:Multiple Imputation for Nonresponse in Surveys}. New York:
Wiley.
{p 4 8 2}
Schafer, J. 1997. {it:Analysis of Incomplete Multivariate Data}. London:
Chapman & Hall.
{p 4 8 2}
van Buuren, S., H. C. Boshuizen and D. L. Knook. 1999. Multiple imputation of
missing blood pressure covariates in survival analysis.
{it:Statistics in Medicine} 18: 681-694.
(Also see http://www.multiple-imputation.com.)
{title:Also see}
{psee}
Online: {helpb ice}
{p_end}

73
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*! version 1.0.3 PR 16oct2004.
* History
* 1.0.3 16oct2004 Saving, using etc of file safest with compound quotes, fixed.
program define mijoin, rclass
version 7
syntax [anything(name=filestub)], CLEAR [ IMPid(string) m(int 0) ]
if "`filestub'"=="" {
capture assert "$mimps"!="" & "$mi_sf"!=""
if _rc {
di as error "please set up your data with -{help miset}-, or specify a filename stub"
exit 198
}
local filestub $mi_sf
if `m'>$mimps {
di as err "m cannot exceed its -miset- value of $mimps"
exit 198
}
if `m'==0 {
local m $mimps
}
}
if `m'<=0 {
di as err "number of imputations m(), if specified, must be a positive integer"
exit 198
}
if "`impid'"=="" {
local J _j
}
else local J `impid'
preserve
quietly {
forvalues j=1/`m' {
use `"`filestub'`j'"', clear
chkrowid
local I `s(I)'
if "`I'"=="" {
* create row number
local I _i
cap drop `I'
gen long `I'=_n
lab var `I' "obs. number"
}
cap drop `J'
gen int `J'=`j'
lab var `J' "imputation number"
tempfile tmp`j'
save `"`tmp`j''"'
}
use `"`tmp1'"', clear
forvalues j=2/`m' {
append using `"`tmp`j''"'
}
char _dta[mi_id] `I'
}
restore, not
end
program define chkrowid, sclass
local I: char _dta[mi_id]
if "`I'"=="" {
exit
}
cap confirm var `I'
if _rc {
exit
}
sret local I `I'
end

102
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{smcl}
{* 11jun2004}{...}
{hline}
help for {cmd:mijoin}, {cmd:misplit}{right:(SJ5-4: st0067_2; SJ5-2: st0067_1; SJ4-3: st0067)}
{hline}
{title:Join or split multiple imputed datasets}
{p 8 17 2}
{c -(}{cmd:mijoin} [{it:filestubname}] | {cmd:misplit}{c )-}{cmd:,}
{cmd:clear}
[{cmd:m(}{it:#}{cmd:)}
{cmdab:imp:id(}{it:varname}{cmd:)}]
{title:Description}
{p 4 4 2}
{cmd:mijoin} converts datasets prepared by Carlin's {helpb miset} routine
to {helpb mvis} format, for analysis by {helpb micombine}. The component
datasets are stacked (joined vertically). If {it:filestubname} is
specified then the program will look for files called
{it:filestubname}{cmd:1.dta}, {it:filestubname}{cmd:2.dta},...,
{it:filestubname}{cmd:m.dta} where {cmd:m} is determined by
the {cmd:m()} option.
{p 4 4 2}
{cmd:misplit} converts a dataset prepared by {helpb mvis}
to {helpb miset} format, for analysis
by {helpb mifit} and other utilities. You should {cmd:use}
the dataset first.
{p 4 4 2}
The operations performed by {cmd:mijoin} and {cmd:misplit}
are reciprocal.
{title:Options}
{p 4 8 2}
{cmd:clear} is not optional and confirms that you are willing to replace the
data in memory.
{p 4 8 2}
{cmd:m(}{it:#}{cmd:)} sets the number of imputed datasets to {it:#}.
{p 4 8 2}
{cmd:impid(}{it:varname}{cmd:)} sets the name of the variable identifying the
imputations to {it:varname} (default is {cmd:_j}). {cmd:mijoin} creates
{it:varname} whereas {cmd:misplit} expects it to exist already.
{title:Remarks}
{p 4 4 2}
After {cmd:mijoin} has been executed, the data are ready for immediate use by
{cmd:micombine}. However, to save the data permanently you would
need to issue a {helpb save} command.
{p 4 4 2}
After {cmd:misplit} has been executed, the data are ready for immediate use by
Carlin's routines. However, to save the data permanently you would
need to issue a {helpb misave} command followed by {helpb miset} on the next use.
{title:Examples}
{p 4 8 2}{cmd:. mvis y x1 x2 x3 using imp, m(3)}{p_end}
{p 4 8 2}{cmd:. use imp, clear}{p_end}
{p 4 8 2}{cmd:. misplit, clear}{p_end}
{p 4 8 2}{cmd:. mifit regress y x1 x2 x3}
{p 4 8 2}{cmd:. miset using imp, clear mimps(5)}{p_end}
{p 4 8 2}{cmd:. mijoin, clear}{p_end}
{p 4 8 2}{cmd:. micombine regress y x1 x2 x3}
{p 4 8 2}{cmd:. mijoin imp, clear m(5)}{p_end}
{p 4 8 2}{cmd:. save imp_mvis}{p_end}
{p 4 8 2}{cmd:. micombine regress y x1 x2 x3}
{title:Author}
{p 4 4 2}
Patrick Royston, MRC Clinical Trials Unit, London.
patrick.royston@ctu.mrc.ac.uk
{title:References}
{p 4 8 2}
Carlin, J. B., N. Li, P. Greenwood, and C. Coffey. 2003.
Tools for analyzing multiple imputed datasets. {it:Stata Journal} 3(3):
226-244.
{title:Also see}
{p 4 13 2}
Online: {helpb mvis}, {helpb micombine}, {helpb miset} (if installed)
{p_end}

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*! version 1.0.1 PR 01oct2004.
program define misplit, rclass
version 7
syntax, CLEAR [ IMPid(string) m(int 5) ]
cap assert `m'>1
if _rc {
di "{err}more than one imputation is required"
exit 198
}
if "`impid'"=="" {
local impid _j
}
cap confirm var `impid'
if _rc {
di as err "imputation identifier `impid' not found in file `using'"
exit 601
}
tempvar J
egen int `J'=group(`impid')
sum `J', meanonly
local m=r(max)
if r(max)<2 {
di as error "more than one imputation is required"
exit 198
}
if r(max)<`m' {
local m=r(max)
di as text "[note: data for only `m' imputations found in file]"
}
preserve
quietly {
forvalues j=1/`m' {
keep if `J'==`j'
drop `J'
cap erase "_mitemp`j'.dta"
save "_mitemp`j'.dta"
restore, preserve
*egen int `J'=group(`impid')
}
global mi_uf `using'
global mi_sf _mitemp
global mimps `m'
use _mitemp1.dta, clear
}
if $mimps==2 {
di "{p}{txt}data for $mimps imputations have been copied to _mitemp1.dta and _mitemp$mimps.dta"
}
else {
di _n "{p}{txt}Data for $mimps imputations have been copied to ${mi_sf}1.dta to $mi_sf$mimps.dta"
}
restore, not
end

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.h mijoin

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*! version 1.1.0 2009-12-22
* - add extended missing values - s. rohrman
capture program drop misschk
program define misschk
version 9.0 // 2009-12-22
syntax [varlist] [if] [in] ///
, [GENerate(string)] /// stub for indicator variables
[dummy] /// dummy indicator variables
[replace] /// replace variables if they exist
[NONUMber] /// . in table for missing, not #
[NOSort] /// Don't sort pattern list
[help] /// explain what is going on
[SPace] /// blank rather than _ if not missing
[EXTmiss] // show extended missing // 1.1.0
tempvar ifin
mark `ifin' `if' `in'
local notmissstr "_"
if ("`space'"=="space") {
local notmissstr " "
}
else if "`nonumber'"=="nonumber" | "`extmiss'"=="extmiss" { // 1.1.0
local notmissstr "-"
}
local sort "sort"
if "`nosort'"=="nosort" {
local sort ""
}
// using extmiss and nonumber options together = error // 1.1.0
if "`nonumber'"=="nonumber" & "`extmiss'"=="extmiss" {
di in red "Error: cannot use nonumber and extmiss options together."
exit
}
// if dummy variable option
if "`dummy'"~="" {
if "`generate'"=="" {
di in red "Error: dummy options requires use of generate option."
exit
}
capture label def lmisschk 0 NotMissing 1 Missing
}
local gennm = "`generate'"
// information on individual variables
di _n in g "Variables examined for missing values"
if "`if'" ~="" | "`in'"~="" {
di in g "For sample defined by: " in y "`if' `in'"
}
// strip off string variables
parse "`varlist'", parse(" ")
local vars
while "`1'" != "" { // loop through variables
capture confirm numeric variable `1'
if _rc==0 {
local vars "`vars' `1'"
}
mac shift
}
local varlist "`vars'"
parse "`varlist'", parse(" ")
local nvar = 0
di _n in g " # Variable # Missing % Missing"
di in g "--------------------------------------------"
quietly tab `ifin'
local total = r(N)
while "`1'" != "" { // loop through variables
local ++nvar
qui count if `1'>=. & `ifin' // count missing; changed == 1.1.0
* create binary variables indicating if observation is missing
if "`dummy'"~="" {
if "`replace'"=="replace" {
capture drop `gennm'`1'
}
quietly gen `gennm'`1' = (`1'>=.) if `ifin' // changed == 1.1.0
label var `gennm'`1' "Missing value for `1'?"
label val `gennm'`1' lmisschk
}
* list # and percent missing
di in y %3.0f " `nvar' " _col(7) "`1'" ///
_col(23) %7.0f r(N) _col(36) %6.1f 100*r(N)/`total'
mac shift
} // loop through list of variables
parse "`varlist'", parse(" ")
// loop through all variables and count missing
tempvar ismissn ismissw missw missn
quietly gen `missn' = 0 if `ifin'
label var `missn' "Missing for how many variables?"
quietly gen str1 `missw' = "" if `ifin'
label var `missw' "Missing for which variables?"
local nvar = 0
local i = 0
local ext "a b c d e f g h i j k l m n o p q r s t u v w x y z" // 1.1.0
while "`1'" != "" {
local ++nvar
* ones has only one's digit of variable number
local ones = mod(`nvar',10)
* drop tempvars from last loop
capture drop `ismissn' `ismissw'
* 1 if mssing, else 0; . if not in if in
capture quietly gen `ismissn' = (`1'>=.) if `ifin' // changed == 1.1.0
* string with indicator of missing status. Space if no missing;
* then if missing, either . or digit number.
capture quietly gen str1 `ismissw' = "`notmissstr'"
if "`nonumber'"=="nonumber" & "`extmiss'"=="" { // 1.1.0
quietly replace `ismissw' = "." if `1'>=. & `ifin' // changed == 1.1.0
}
else if "`extmiss'"=="extmiss" & "`nonumber'"=="" { // changed == 1.1.0
quietly replace `ismissw' = "." if `1'==. & `ifin'
foreach ltr in `ext' {
quietly replace `ismissw' = "`ltr'" if `1'==.`ltr' & `ifin'
}
}
else if "`nonumber'"=="" & "`extmiss'"=="" {
quietly replace `ismissw' = "`ones'" if `1'>=. & `ifin' // changed == 1.1.0
}
* add blank every 5th variable
if mod(`nvar'-1,5) == 0 {
quietly replace `missw' = `missw' + " "
}
* build string with pattern of missing data
quietly replace `missw' = `missw' + `ismissw'
* count total number of missing for given case
quietly replace `missn' = `missn' + `ismissn'
mac shift
}
capture drop `ismissn' `ismissw'
// List results
* patterns of missing data
if "`extmiss'"=="" { // 1.1.0
di _n in y "Warning: " in g ///
"this output does not differentiate among extended missing."
di in g ///
"To generate patterns for extended missing, use extmiss option."
}
if "`help'"=="help" {
di in g _n ///
"The columns in the table below correspond to the # in the table above."
di in g ///
"If a column is blank, there were no missing cases for that variable."
}
if "`help'"=="help" & "`extmiss'"=="extmiss" { //1.1.0
di _n
di _n in g ///
"Letters in each column refer to the extended missing for the"
di in g ///
"corresponding variable"
}
tab `missw' if `ifin', `nosort'
* number missing for given observations
if "`help'"=="help" {
di _n in g ///
"Table indicates the number of variables for which an observation"
di in g ///
"has missing data."
}
tab `missn' if `ifin'
// create variables with pattern and number of missing cases
if "`generate'" != "" {
if "`replace'"=="replace" {
capture drop `gennm'pattern
capture drop `gennm'number
}
clonevar `gennm'pattern = `missw'
clonevar `gennm'number = `missn'
if "`help'"=="help" {
di _n in g "Variables created:" // added end quote 1.1.0
di in y " `gennm'pattern" in g ///
" is a string variable showing the pattern of missing data."
di in y " `gennm'number" in g ///
" is the number of variables for which a case has missing data."
di in y " `gennm'<varnm>" in g ///
" is a binary variable indicating missing data for <varnm>."
}
}
end
exit
* version 1.0.1 06Jul2005
* version 1.0.0 13Apr2005

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{smcl}
{* 22Dec2009}{...}
{cmd:help misschk}: Help for misschk - 2009-12-22
{hline}
{p2colset 4 14 14 2}{...}
{title:Overview}
{p 4 4 2 75}
The command {cmd:misschk} examines patterns of missing data for a set of variables.
pattern of missing data.
{title:Syntax}
{p 8 13 2}
{cmdab:misschk} [{varlist}]
{ifin}
{weight}
[{cmd:,}
{it:options}]
, [GENerate(string)] /// stub for indicator variables
[dummy] /// dummy indicator variables
[replace] /// replace variables if they exist
[NONUMber] /// . in table for missing, not #
[NOSort] /// Don't sort pattern list
[help] /// explain what is going on
[SPace] /// blank rather than _ if not missing
[EXTmiss] // show extended missing // 1.1.0
{synoptset 15 tabbed}{...}
{synopthdr}
{synoptline}
{synopt:{opt ext:miss}}Each type of extended missing values is indicated
with the letter for that missing value (e.g., {bf:c} for {bf:.c}). By
default all missing values are treated simply as missing and
indicated by a {bf:.}.{p_end}
{synopt:{opt gen:erate(rootname)}}is the root for the variables created with
information about missing data. If this option is not used,
temporary variables are created that are deleted when the program is
finsihed. The variables created are:
{it:rootname}{bf:n} is the number of variables from the variable-list for which
a given observation has missing data. For example, a value of 5 would
mean that that observation had missing data for five of the variables
in the list.
{it:rootname}{bf:which} indicates the pattern of missing values. This is a
string variable with a {bf:_} indicating valid data for a variable
and a number indicating missing data for that variable. For
example, {bf:_____ __8__ _} is the pattern in which there is no
missing data for the first seven variables in the variablae
list, missing data for the eighth, and no missing data for the
ninth through eleventh variable.{p_end}
{synopt:{opt dummy}}requests that dummy variables be created for each
variable in the variable list. The dummy variable begins with the
stem specified with the {bf:gen()} options, then adds the name of the
variable. A value of 1 indicates missing data for that case, 0 indicates
data is not missing. For example, with the options {bf:gen(M_) dummy},
variables such as M_female M_income would be generated.{p_end}
{synopt:{opt nonumber}}specifies that a variable that has missing cases will be
indicated by a {bf:.} rather than by a single digit number corresponding
to the sequence number of that variable. For example, without the
{bf:nonumber} option, a missing data pattern might look like
{bf:_2_4_ 6___} to indicate missing data in the 2nd, 4th and 6th
variables. With the {bf:nonumber} option, the pattern
would be {bf:_._._ .___} .{p_end}
{synopt:{opt nosort}}specifies that the list of patterns of missing data
should not be sorted with the most common pattern listed first. With
{bf:nosort}, the patterns are listed according to the pattern, not the
frequency of missing data.{p_end}
{synopt:{opt replace}}replaces existing variables {it:rootname}{bf:n}
and {it:rootname}{bf:which} if they already exist.{p_end}
{synopt:{opt space}}indicates that in the summary table a space rather than
a {bf:_} will be used to indicate when a variable does not have missing data.{p_end}
{synopt:{opt help}}requests a description of each part of the output.{p_end}
{synoptline}
INCLUDE help spost_footer

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*! version 2.5.0 2009-10-28 jsl
* - stata 11 update for returns from -mlogit-
capture program drop mlogplot
capture program drop lp_plt
capture program drop getprob
program define mlogplot
version 6.0
tempname tmp b sd bstd sdrhs 01rhs sdi ch0 chu chs _lpstdt _lpplt isdum
* if 1, then auto exit of mlogview
global PE_mlerr = 0
/* 06Apr2006 - trap 0 category
mat `tmp' = e(cat)
local ncat = e(k_cat)
local i = 1
while `i' <= `ncat' {
local v = `tmp'[`i',1]
if `v' <= 0 {
di in r "outcome categories cannot be 0 or negative."
di in r "recode outcome variable to begin at 1."
exit
}
local i = `i' + 1
}
*/
* 18Nov2005
local maxvar = 7
syntax [varlist(default=none)], [/*
*/ ORatio DChange Prob(real 1) packed labels /*
*/ min(real -99999) max(real 99999) /*
*/ matrix Std(string) Vars(string) /*
*/ dcadd(real 0) dcbase(real .1) NTics(real 7) /*
*/ Basecategory(real -1) Note(string) VALues] /*
*/ [saving(string)] [sign]
* create saving option
local gphopen "gph open"
if "`saving'"~="" { local gphopen "gph open, saving(`saving')" }
local varis `varlist'
if "`varis'"=="" & "`matrix'"~="matrix" {
_perhs
local varis "`r(rhsnms)'"
}
if `min'>=`max' {
di in r "the minimum is larger than the maximum"
exit
}
* dcplot=1 if dc plot
local dcplot = 0
if "`dchange'"=="dchange" & "`oratio'"~="oratio" {
local dcplot = 1
local packed packed
}
* mavar depends on whether there is packing
if "`packed'" == "packed" {
local maxvar = 11 /* max # to plot in packed graph */
}
*=> matrix input
if "`matrix'" == "matrix" {
if `prob'~=1 {
di in r "with matrix input the prob " /*
*/ "option does not work."
local prob = 1
}
if "`dchange'" == "dchange" {
if "`oratio'" ~= "oratio" {
di in r "dchange plots are not " /*
*/ "possible with matrix input."
exit
}
di in r "dchange option does not work " /*
*/ "with matrix output. option was ignored."
local dchange ""
}
local depvar "$mnldepnm"
* names of variables to plot
local varis `vars'
* in b, cols are variables, rows are contrasts
mat `b' = mnlbeta
mat `sdrhs' = mnlsd
local nvarp1 = colsof(`b') + 1 /* with constant */
*todo: check if # of bvarnm is same as cols of b
* variables associated with rows of betas
local bvarnm "$mnlname"
*todo: if doesn't exist, make it all 1's
* columns correspond to columns in betas
mat `sd' = mnlsd
* check size of beta and sd's
local n1 = colsof(`b')
local n2 = colsof(`sd')
if `n1'~=`n2' {
di in r "# of columns in mnlbeta and mnlsd must be equal"
exit
}
} /* if matrix */
*=> get information from mlogit
else {
if "`e(cmd)'"!="mlogit" {
di in r "mlogplot must be run after mlogit or with matrix input"
exit
}
local depvar = e(depvar)
version 5
mat `b' = get(_b)
version 6
* 2009-10-28
tempname eV
_get_mlogit_bv `b' `eV'
*mat list `b'
*mat list `eV'
local bvarnm : colnames(`b')
parse "`bvarnm'", parse (" ")
quietly _pesum, dummy
mat `sdrhs' = r(Ssd)
mat `sdrhs' = `sdrhs'[1,2...]
mat `01rhs' = r(Sdummy)
mat `01rhs' = `01rhs'[1,2...]
* 16Nov2005 1.6.8 - get range
tempname rngrhs
mat `rngrhs' = r(Smax) - r(Smin)
mat `rngrhs' = `rngrhs'[1,2...]
*<
local nvarp1 = colsof(`b') /* with constant */
}
local nvar = `nvarp1' - 1 /* excludes constant */
local ncat = rowsof(`b') + 1
local maxnvar = 3*`nvar' /* allows each var plotted 3 times */
/* 0->1, +-1, +-sd */
if `basecategory' > `ncat' {
di in r "specified base category exceeds number of categories"
exit
}
*=> get discrete change coefficints
*
* Note: PE_dc - each variable has 5 rows
* row 1: 0->1 (16Nov2005 1.6.8 not min->max)
* row 2: min->max (16Nov2005 1.6.8 which can be 0->1)
* row 3: -/+ 1
* row 4: -/+ sd
* row 5: not used
* row label is void if coefficient not computed
* 16Nov2005 1.6.8 - discrete change over range
tempname chr
mat `chr' = J(`nvar',`ncat',0)
mat `ch0' = J(`nvar',`ncat',0)
mat `chu' = J(`nvar',`ncat',0)
mat `chs' = J(`nvar',`ncat',0)
if "`dchange'" == "dchange" {
capture local ncol = colsof(PE_dc)
capture local nrow = rowsof(PE_dc)
if "`ncol'"=="" {
di in r "you must run prchange before " /*
*/ "plotting discrete changes"
global PE_mlerr = 1
exit
}
local nr = `nvar'*5
/* TEST:
di "nvar: `nvar'"
di "nvarp1: `nvarp1'"
di "ncat: `ncat'"
di "maxnvar:`maxnvar'"
di "ncol: `ncol'"
di "nrow: `nrow'"
di "nr: `nr'"
END TEST */
* size of PE_dc is wrong; probably old PE_dc hanging around
* 26Nov2006 - or prchange run on only some of the rhs variables
if `ncol'~=`ncat' | `nrow'~=`nr' {
di in r "There is a problem with the discrete change " ///
"coefficients. Rerun " in w "prchange" in r "."
di in r "Note that " in w "prchange" in r " must include " ///
"all variables. For example, for the model "
di in w "mlogit occ white ed exper" in r ", run " ///
in w "prchange" in r ", not " in w "prchange white ed" ///
in r "."
global PE_mlerr = 1
exit
}
local ivar = 1
local r = 1
while `ivar' <= `nvar' {
local c = 1
* loop over outcome categories
while `c' <= `ncat' {
* 16Nov2005 1.6.8 - retrieve dc over range
scalar `tmp' = PE_dc[`r'+1,`c']
mat `chr'[`ivar',`c'] = `tmp'
* 16Nov2005 1.6.8 retrieve dc from 0 to 1 (same as dc over range)
scalar `tmp' = PE_dc[`r',`c']
*scalar `tmp' = PE_dc[`r'+1,`c']
mat `ch0'[`ivar',`c'] = `tmp'
* -/+ 1
scalar `tmp' = PE_dc[`r'+2,`c']
mat `chu'[`ivar',`c'] = `tmp'
* -/+ sd
scalar `tmp' = PE_dc[`r'+3,`c']
mat `chs'[`ivar',`c'] = `tmp'
local c = `c' + 1
}
local ivar = `ivar' + 1
local r = `r' + 5
}
} /* if dchange */
*=> parse variables to plot and check if dummy variable
mat `_lpplt' = J(`maxnvar',1,0) /* 1 if plot this coef */
mat `isdum' = J(`maxnvar',1,0) /* 1 if dummy variable */
parse "`varis'",parse(" ") /* varis==variables to be plot */
local ntoplot = 0
while "`1'"~="" {
if `ntoplot' >= `maxvar' {
di in r "only `maxvar' variables can be " /*
*/ "plotted in one graph"
exit
}
* check selected name is in beta matrix
local i 1
local okvarn = 0
while `i' <= `nvar' {
* bvarnm contains names from beta matrix
local vnm : word `i' of `bvarnm'
if "`1'"=="`vnm'" { local okvarn = `i' }
local i = `i' + 1
}
if `okvarn'==0 {
di in red "`1' was not a variable in mlogit"
exit
}
local ntoplot = `ntoplot' + 1
if "`matrix'" ~= "matrix" {
mat `isdum'[`ntoplot',1] = `01rhs'[1,`okvarn']
}
* plot variables in this order
mat `_lpplt'[`ntoplot',1] = `okvarn'
macro shift
}
*=> parse standardization type for each variable
mat `_lpstdt' = J(`maxnvar',1,0) /* Plot: 1=unstd; 3=std; 2=0/1 */
local i 1
local nstd = length("`std'")
while `i' <= `nstd' {
local stdi = substr("`std'",`i',1)
* 16Nov2005 1.6.8 - allow r as an option for how to plot variable
if "`stdi'"~="r" & "`stdi'"~="s" & "`stdi'"~="u" & "`stdi'"~="0" {
di in r "std() options must be s, r, u or 0"
* if "`stdi'"~="s" & "`stdi'"~="u" & "`stdi'"~="0" {
* di in r "std() options must be s, u or 0"
exit
}
mat `_lpstdt'[`i',1] = 1 /* unstd */
if "`stdi'" == "s" {
mat `_lpstdt'[`i',1] = 3 /* std */
}
* 16Nov2005 1.6.8 - if range, type 4 coefficients
if "`stdi'" == "r" {
mat `_lpstdt'[`i',1] = 4 /* min max */
}
if "`stdi'" == "0" {
mat `_lpstdt'[`i',1] = 2 /* dummy */
if `isdum'[`i',1]~=1 {
di in r "variable specified as std(0) " /*
*/ "is not a binary variable"
exit
}
}
* warning if isdum and not 0
if `isdum'[`i',1]==1 & `_lpstdt'[`i',1]~=2 {
local tmp = `_lpplt'[`i',1]
local vnm : word `tmp' of `bvarnm'
di in r "warning: variable `vnm'" /*
*/ " is binary, but std(0) was not used"
}
local i = `i' + 1
}
if `ntoplot'~=`nstd' {
di in r "# of variables does not match # of " /*
*/ "std() values specified"
exit
}
*=> set up matrices with plot data
tempname _lpbplt _lpbvar _lpbcat _lpboff _lpbtyp _lpbchk _lpbchp
local tmp = `ntoplot' * `ncat' /* # coefs to plot */
matrix `_lpbplt' = J(`tmp',1,0) /* coef to plot */
matrix `_lpbchp' = `_lpbplt' /* values of discrete change */
matrix `_lpboff' = `_lpbplt' /* vertical offset */
matrix `_lpbvar' = `_lpbplt' /* variable number */
matrix `_lpbcat' = `_lpbplt' /* category */
matrix `_lpbtyp' = `_lpbplt' /* type of standardization */
* compute standardized betas
mat `bstd' = `b'
local ivar = 1
while `ivar' < `nvarp1' {
scalar `sdi' = `sdrhs'[1,`ivar']
* loop over categories and fill in border
local icat = 1
while `icat' < `ncat' {
mat `bstd'[`icat',`ivar'] = `b'[`icat',`ivar'] * `sdi'
local icat = `icat' + 1
}
local ivar = `ivar' + 1
}
* 2007-06-29
if "`matrix'" != "matrix" {
* 16Nov2005 1.6.8 - compute beta*range
tempname brng
mat `brng' = `b'
local ivar = 1
while `ivar' < `nvarp1' {
scalar `sdi' = `rngrhs'[1,`ivar']
* loop over categories and fill in border
local icat = 1
while `icat' < `ncat' {
mat `brng'[`icat',`ivar'] = `b'[`icat',`ivar'] * `sdi'
local icat = `icat' + 1
}
local ivar = `ivar' + 1
}
}
else {
// make range missing since it is not used
tempname brng
mat `brng' = `bstd' * .
} // 2007-06-29
* determine basecategory
if "`matrix'"~="matrix" {
_pecats `e(depvar)'
local catnm "`r(catnms)'"
* 30Mar2005 1.6.6
if "`values'"=="values" {
local catnm "`r(catvals)'"
}
* refnum is value of the reference category for the estiamted betas
* 2007-06-29 stata 10
if c(stata_version) < 10 {
local refnum = e(basecat)
}
else {
local refnum = e(baseout)
}
* 1.7.0 08Apr2006 - fix when refnum is 0
if `refnum'==0 {
local refnum = 1
}
* names of outcome categories in order of matrix
* all but the last correspond to the rows in b
}
if "`matrix'"=="matrix" {
local catnm "$mnlcatnm"
local refnum "`ncat'"
}
*=> stack b coefficients to plot into _lpbplt vector
* determine offset for plot
* baserow has the row in b with beta's for new base category
local baserow = 0
* `basecategory' has the # of the specified base
if `basecategory'~=`refnum' & `basecategory'~=-1 {
local baserow = `basecategory'
if `basecategory' > `refnum' { local baserow = `baserow' - 1 }
}
* loop through variables, decide on std and unstd to plot
local iloc = 1
local ivar = 1
while `ivar' <= `ntoplot' {
local icat = 1
local varnum = `_lpplt'[`ivar',1]
* find b values for changing basecategory
local bbase = 0
local bbstd = 0
* 16Nov2005 1.6.8
local bbrng = 0
if `baserow' ~= 0 {
local bbase = `b'[`baserow',`varnum']
local bbstd = `bstd'[`baserow',`varnum']
* 16Nov2005 1.6.8
local bbrng = `brng'[`baserow',`varnum']
}
* compute _lpbplt
while `icat' <= `ncat' {
if `icat' < `ncat' {
matrix `_lpbplt'[`iloc',1] = `b'[`icat',`varnum'] - `bbase'
}
else {
matrix `_lpbplt'[`iloc',1] = 0 - `bbase'/* ref category */
}
matrix `_lpbtyp'[`iloc',1] = 1
matrix `_lpbchp'[`iloc',1] = `chu'[`varnum',`icat']
if `_lpstdt'[`ivar',1] == 2 {
matrix `_lpbtyp'[`iloc',1] = 2
matrix `_lpbchp'[`iloc',1] = `ch0'[`varnum',`icat']
}
if `_lpstdt'[`ivar',1] == 3 {
if `icat' < `ncat' {
matrix `_lpbplt'[`iloc',1] = /*
*/ `bstd'[`icat',`varnum'] - `bbstd'
}
else {
matrix `_lpbplt'[`iloc',1] = /*
*/ 0 - `bbstd' /* reference category */
}
matrix `_lpbtyp'[`iloc',1] = 3
matrix `_lpbchp'[`iloc',1] = `chs'[`varnum',`icat']
}
* 16Nov2005 1.6.8 - coefficients when change over range
if `_lpstdt'[`ivar',1] == 4 {
if `icat' < `ncat' {
matrix `_lpbplt'[`iloc',1] = /*
*/ `brng'[`icat',`varnum'] - `bbrng'
}
else {
matrix `_lpbplt'[`iloc',1] = /*
*/ 0 - `bbrng' /* reference category */
}
matrix `_lpbtyp'[`iloc',1] = 4
matrix `_lpbchp'[`iloc',1] = `chr'[`varnum',`icat']
}
* _lpbplt now has coefficients to be plotted
matrix `_lpbvar'[`iloc',1] = `ivar' /* var# for given coef */
matrix `_lpbcat'[`iloc',1] = `icat' /* cat# for tiven coef */
local iloc = `iloc' + 1
local icat = `icat' + 1
}
local ivar = `ivar' + 1
}
if `dcplot' == 1 { /* dcplot , so plot _lpbchp */
matrix `_lpbplt' = `_lpbchp'
}
*=> determine offsets for letters
* algorithm is use offsets of 0,1,2,0,1,2 as coefficients are ordered
* from smallest to largerst. The code is messy, but works...
* if packed, use 0's already in _lpboff
if "`packed'" ~= "packed" {
local iloc = 1
local ivar = 1
while `ivar' <= `ntoplot' {
local varnum = `_lpplt'[`ivar',1]
* grab coefficients for given variable in _lpbplt
local istrt = ((`ivar'-1)*`ncat') + 1
local iend = `istrt' + `ncat' - 1
matrix `_lpbchk' = `_lpbplt'[`istrt'..`iend',1]
local icat = 1
while `icat' <= `ncat' {
local icat2 = 1
local minn = 99999999
* find smallest coef that hasn't been used
while `icat2' <= `ncat' {
if `_lpbchk'[`icat2',1] < `minn' {
local minn = `_lpbchk'[`icat2',1]
local minloc = `icat2'
}
local icat2 = `icat2' + 1
}
* this is current smallest
local updown = mod(`icat'+2,3)
* change to big values so won't select again
mat `_lpbchk'[`minloc',1] = 99999999
local minn = 99999999
* this is the location in the stored variables
local ichng = `istrt' + `minloc' - 1
mat `_lpboff'[`ichng',1] = `updown'
local iloc = `iloc' + 1
local icat = `icat' + 1
}
local ivar = `ivar' + 1
}
}
*=> create variables from the matrices
mat colnames `_lpbplt' = _lpbplt
mat colnames `_lpbvar' = _lpbvar
mat colnames `_lpbcat' = _lpbcat
mat colnames `_lpboff' = _lpboff
mat colnames `_lpbtyp' = _lpbtyp
mat colnames `_lpbchp' = _lpbchp
mat colnames `_lpplt' = _lpplt
mat colnames `_lpstdt' = _lpstdt
svmat `_lpbplt', names(col)
svmat `_lpbvar', names(col)
svmat `_lpbcat', names(col)
svmat `_lpboff', names(col)
svmat `_lpbtyp', names(col)
svmat `_lpbchp', names(col)
svmat `_lpplt', names(col)
svmat `_lpstdt', names(col)
*=> generate variable with first letter of category names
if "`matrix'"~="matrix" {
local ltr : word `refnum' of `catnm'
local ltr = upper(substr("`ltr'",1,1))
quietly generate str1 _lpbltr = "`ltr'"
local icat = 1
while `icat' <= `ncat' {
if `icat'==`refnum' {
local refnm: word `ncat' of `catnm'
if `baserow'~=0 {
local refnm: word `baserow' of `catnm'
}
}
if `icat'!=`refnum' {
local nm: word `icat' of `catnm'
local ltr = upper(substr("`nm'",1,1))
quietly replace _lpbltr = "`ltr'" if _lpbcat == `icat'
}
local icat = `icat' + 1
}
}
if "`matrix'"=="matrix" {
* this is the reference category in the betas
local ltr : word `ncat' of `catnm'
local ltr = upper(substr("`ltr'",1,1))
quietly generate str1 _lpbltr = "`ltr'"
* get letter for reference category
local baserow = `basecategory'
if `baserow' == -1 { local baserow = `ncat' }
local refnm : word `baserow' of `catnm'
* place correct letters to plot
local icat = 1
while `icat' <= `ncat' {
if `icat'!=`refnum' {
local nm: word `icat' of `catnm'
local ltr = upper(substr("`nm'",1,1))
quietly replace _lpbltr = "`ltr'" if _lpbcat == `icat'
}
local icat = `icat' + 1
}
}
*=> data to pass to plot program
global S_1 = `dcplot'
global S_2 = `min'
global S_3 = `max'
global S_4 "`packed'"
global S_5 = `ntoplot'
global S_6 = `dcadd'
global S_7 = `dcbase'
global S_8 = `ncat'
global S_9 "`labels'"
global S_10 "`dchange'"
global S_11 "`bvarnm'"
global S_12 = `prob'
global S_13 "`refnm'"
global S_14 = `ntics'
global S_15 "`note'"
global S_16 "`depvar'"
global S_17 "`gphopen'"
* 16Nov2005 sign
global S_18 = 0
if "`sign'"=="sign" {
global S_18 = 1
}
lp_plt
capture drop _lpbplt-_lprowb
end
program define lp_plt
* decode information from calling program
local dcplot = $S_1
local min = $S_2
local max = $S_3
local packed "$S_4"
local ntoplot = $S_5
local dcadd = $S_6
local dcbase = $S_7
local ncat = $S_8
local labels "$S_9"
local dchange "$S_10"
local bvarnm "$S_11"
local prob = $S_12
local refnm "$S_13"
local ntics = $S_14
local note "$S_15"
local depvar "$S_16"
local gphopen "$S_17"
* 16Nov2005 sign
local addsign = $S_18
* coordinate used:
*
* (0,0) (0,cstrt) (0,cend)
*
* (rhead,cstrt) data (rhead,cend)
*
* (rhead+rvar,cstrt) (rhead+rvar,cend)
*
local cstrt = 10000 /* first column in plot space */
local cborder = 1000 /* border at ends of plot space */
local cend = 31500 /* right most column of plot space */
local crange = `cend' - `cstrt' - (2*`cborder')
local cname = `cstrt' - 700 /* name of variable ends here */
*local rhead = 2000 /* header space for factor change scale */
local rhead = 1800 /* header space for factor change scale */
if `dcplot'==1 { local rhead = 1000 }
if "`note'"~="" {
local rhead = `rhead' + 1000
}
* rows per variable in plot space
* local rvar = 3600 /* maxvar = 5 */
* local rvar = 3200 /* maxvar = 6 */
local rvar = 2800 /* maxvar = 7 */
if "`packed'" == "packed" { local rvar = 1700 }
local rnmoff = 1000 - `rvar' /* vert offset for listing name */
* vert offset within horizontal lines (larger adds vertical compression
local rltoff = 800
* rescale to metric used in columns of plot and find 0 location
quietly sum _lpbplt /* get min and max of coefficients being plotted */
local minb = _result(5)
if `min'~=-99999 & `min'<`minb' { local minb = `min' }
local maxb = _result(6)
if `max'~=99999 & `max'>`maxb' { local maxb = `max' }
local rng = `maxb' - `minb'
* change range to [0-1]
quietly generate _lpcolb = (_lpbplt - `minb')/`rng'
local c0 = (0 - `minb')/`rng'
* min and max column for plotting area
local minn = `cstrt' + `cborder'
local maxx = `cend' - `cborder'
local rng = `maxx' - `minn'
quietly replace _lpcolb = (_lpcolb * `rng') + `minn'
local c0 = (`c0' * `rng') + `minn'
* rescale metric for rows in plot
* # of rows to contain letters
local rltr = `rvar' - `rltoff' - `rltoff'
if "`packed'" ~= "packed" {
quietly sum _lpboff
local minn = _result(5)
local maxx = _result(6)
local rng = `maxx' - `minn'
* change to range of 0--1
quietly generate _lprowb = (_lpboff - `minn')/`rng'
* expand to range of rltr
quietly replace _lprowb = _lprowb * `rltr' /* offsets as # of rows */
* determine row position
#delimit ;
quietly replace _lprowb = _lprowb
+ `rhead' /* skip over header */
+ ((_lpbvar-1)*`rvar') /* space over prior variables */
+ `rltoff' /* space before letters begin */
;
#delimit cr
quietly sum _lprowb
}
else if "`packed'" == "packed" {
local rltoff = 925 /* vert offset top & bot for letters */
* determine row position
#delimit ;
quietly generate _lprowb = `rhead' /* skip over header */
+ ((_lpbvar-1)*`rvar') /* space over prior variables */
+ `rltoff' /* space before letters begin */
;
#delimit cr
}
`gphopen'
gph font 600 300
if "`note'"~="" {
local tmp = `cstrt' - 400
gph text 480 `tmp' 0 -1 `note'
}
local rloc = `rhead' - 200
* axis at top of graph
gph line `rloc' `cstrt' `rloc' `cend'
* add labels at top and bottom
gph font 500 250
local rloc = `rhead' - 1400
if `dcplot' == 0 {
local ltr "`refnm'"
* move top label down a smidge
local rloctmp = `rloc' + 250
gph text `rloctmp' `cstrt' 0 -1 /*
*/ Factor Change Scale Relative to Category `ltr'
local rloc = `rhead' + (`ntoplot'*`rvar') + 1200
local tmp ""
* if "`depvar'" ~= "" { local tmp " of `depvar'" }
local ltr "`refnm' `tmp'"
gph text `rloc' `cstrt' 0 -1 /*
*/ Logit Coefficient Scale Relative to Category `ltr'
}
if `dcplot' == 1 {
local rloc = `rhead' + (`ntoplot'*`rvar') + 1200
*! version 1.6.5 2/24/2003 - correct typo in graph
local tmp "Change in Predicted Probability for $S_16"
gph text `rloc' `cstrt' 0 -1 `tmp'
}
* add tic marks
local nper = (`maxb' - `minb')/(`ntics' - 1)
local itic = 1
local minn = `cstrt' + `cborder'
local maxx = `cend' - `cborder'
local cper = (`maxx' - `minn')/(`ntics' - 1)
while `itic' <= `ntics' {
local ticval : display round(exp(`minb' + ((`itic'-1)*`nper')),.01)
local cval = `minn' + ((`itic'-1)*`cper')
local rloc = `rhead' - 600
* write tic value at top
local rloctmp = `rloc' + 200 /* move it down a smidge */
if `dcplot' ==0 { gph text `rloctmp' `cval' 0 0 `ticval' }
* add tic marks
local rlst = `rhead' - 200
local rlend = `rlst' + 150
gph line `rlst' `cval' `rlend' `cval'
local rlst = `rhead' + (`ntoplot'*`rvar') - 200
local rlend = `rlst' - 150
gph line `rlst' `cval' `rlend' `cval'
local ticval : display round(`minb' + ((`itic'-1)*`nper'),.01)
local itic = `itic' + 1
local rloc = `rhead' + (`ntoplot'*`rvar') + 400
gph text `rloc' `cval' 0 0 `ticval'
}
local rloc = `rhead' + (`ntoplot'*`rvar') - 200
gph line `rloc' `cstrt' `rloc' `cend'
if `dcplot' == 1 {
local rt = `rhead' - 200
gph line `rt' `c0' `rloc' `c0'
}
* plot letters
local i 0
local ivar 1
local dcbase = sqrt(`dcbase')
while `ivar' <= `ntoplot' { /* loop over variables */
local icat 1
while `icat' <= `ncat' { /* loop over categories within vars */
local i = `i' + 1
* get point to plot
local r = _lprowb[`i']
local c = _lpcolb[`i']
local l = _lpbltr[`i']
local siz 1
if "`dchange'" == "dchange" & `dcplot' == 0 {
local siz = sqrt(`dcadd'+abs(_lpbchp[`i']))/`dcbase'
}
local fr = `siz'*700
local fc = `siz'*350
gph font `fr' `fc'
* 16Nov2005 option sign
if _lpbchp[`i']<0 & `addsign'==1 {
gph text `r' `c' 0 0 _
* local l "-`l'"
}
gph text `r' `c' 0 0 `l'
local icat = `icat' + 1
}
local ivar = `ivar' + 1
}
* add names and dividing lines
local ivar = 1
while `ivar' <= `ntoplot' {
* name of variable
local varnum = _lpplt[`ivar']
if `dcplot' == 0 & `prob'~= 1 {
* get prob values
global S_1 = `varnum' /* variable number */
global S_2 = `ncat'
getprob
local cat1 2
while `cat1' <= `ncat' {
* location in data set of coordinates for this coef
local loc1 = ((`ivar'-1)*`ncat')+`cat1'
local r1 = _lprowb[`loc1']
local c1 = _lpcolb[`loc1']
local cat2 1
while `cat2' < `cat1' {
local loc2 = ((`ivar'-1)*`ncat')+`cat2'
local r2 = _lprowb[`loc2']
local c2 = _lpcolb[`loc2']
local p = mlplt_p[`cat1',`cat2']
if `p' > `prob' { gph line `r1' `c1' `r2' `c2' }
local cat2 = `cat2' + 1
} /* `cat2' < `cat1' */
local cat1 = `cat1' + 1
} /* `cat1' < `ncat' */
} /* if get prob */
local vname: word `varnum' of `bvarnm'
if "`labels'" == "labels" & "`matrix'"~="matrix" {
local vname2 : variable label `vname'
if "`vname2'"!="" { local vname "`vname2'"}
}
local rloc = `rhead' + `rnmoff' + (`ivar'*`rvar')
gph font 700 350
local _lpstdt = _lpstdt[`ivar']
if "`packed'" == "packed" {
if `_lpstdt' == 3 {
gph text `rloc' `cname' 0 1 `vname'-std
}
else if `_lpstdt' == 2 {
gph text `rloc' `cname' 0 1 `vname'-0/1
}
else if `_lpstdt' == 1 {
gph text `rloc' `cname' 0 1 `vname'
}
* 16Nov2005 1.6.8 - label for dc over range
else if `_lpstdt' == 4 {
gph text `rloc' `cname' 0 1 `vname'-range
}
}
if "`packed'" ~= "packed" {
gph text `rloc' `cname' 0 1 `vname'
gph font 400 200
local rloc = `rhead' + `rnmoff' + (`ivar'*`rvar') + 900
if `_lpstdt' == 1 {
gph text `rloc' `cname' 0 1 UnStd Coef
}
if `_lpstdt' == 2 {
gph text `rloc' `cname' 0 1 0/1
}
if `_lpstdt' == 3 {
gph text `rloc' `cname' 0 1 Std Coef
}
* 16Nov2005 1.6.8 - label for or over range
else if `_lpstdt' == 4 {
gph text `rloc' `cname' 0 1 Range Coef
}
}
* dividing line
if `ivar' != 1 {
local rloc = `rhead' + ((`ivar'-1)*`rvar') - 200
gph line `rloc' `cstrt' `rloc' `cend'
}
local ivar = `ivar' + 1
}
gph close
end
program define getprob
* get the prob values for all contrasts
version 5.0
tempname b v b1 b2 b12 se z p
local ivar = $S_1
local ncat = $S_2
matrix `b' = get(_b)
local nvars = colsof(`b')
mat `v' = get(VCE)
* 2009-10-28 - fix for stata 11 returns
nobreak {
_get_mlogit_bv `b' `v'
local nvars = colsof(`b')
}
/* TEST
di "nvars: `nvars'"
di "ncat: `ncat'"
di "v with fix"
mat list `v'
di "b with fix"
mat list `b'
END TEST */
matrix mlplt_p = J(`ncat',`ncat',1)
* loop through all pairs of categories
local cat1 1
local cat2 1
while `cat1' <= `ncat' {
while `cat2' <= `cat1' {
if `cat1'!=`cat2' {
* 1st element of contrast is not ref cat
if `cat1'!=`ncat' {
if `cat2'==`ncat' { scalar `b2' = 0 }
else { scalar `b2' = `b'[`cat2',`ivar'] }
scalar `b1' = `b'[`cat1',`ivar']
scalar `b12' = `b1'-`b2'
local loc2 = ((`cat2'-1)*`nvars')+`ivar'
if `cat2'==`ncat' {
scalar `se' = sqrt(`v'[`loc1',`loc1'])
}
else {
local loc1 = ((`cat1'-1)*`nvars')+`ivar'
scalar `se' = sqrt(`v'[`loc1',`loc1'] + /*
*/ `v'[`loc2',`loc2'] - 2*`v'[`loc1',`loc2'])
}
} /* `cat1'!=`ncat' */
* first element of contrast is reference category
if `cat1'==`ncat' {
if `cat2'==`ncat' { scalar `b2' = 0 }
else { scalar `b2' = `b'[`cat2',`ivar'] }
scalar `b1' = 0 /*`b'[`cat1',`ivar']*/
scalar `b12' = `b1'-`b2'
local loc2 = ((`cat2'-1)*`nvars')+`ivar'
scalar `se' = sqrt(`v'[`loc2',`loc2'])
}
scalar `se' = 1/`se'
scalar `z' = `se'*`b12'
scalar `p' = 2*normprob(-abs(`z'))
matrix mlplt_p[`cat1',`cat2'] = `p'
matrix mlplt_p[`cat2',`cat1'] = `p'
} /* if `cat1'!=`cat2' */
local cat2 = `cat2' + 1
} /* while `cat2' <= `ncat' */
local cat2 1
local cat1 = `cat1' + 1
} /* cat1 */
end
exit
* version 1.6.4 3/22/2001
* version 1.6.5 2/24/2003 - correct typo in graph
* version 1.6.6 30Mar2005 - add values option
* version 1.6.7 13Apr2005
* version 1.6.8 allow plots for change over range 16Nov2005
* add: option sign: underline negative discrete change
* option r : for range
* version 1.6.9 error if outcome cat <= 0
* version 1.7.0 08Apr2006 fixed when basecategory is 0!
* version 1.7.1 26Nov2006
* improve error message when prchange is a problem.
* version 1.7.2 29Jun2007 // fix range problem if data is a matrix
* version 1.7.3 29Jun2007 // stata 10 revisions

157
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@ -0,0 +1,157 @@
.-
help for ^mlogplot^ - 03Nov2005
.-
Odds ratio and discrete change plots for multinomial logit
----------------------------------------------------------
For odds ratio plots, the results of ^mlogit^ must be in memory; for discrete
change plots or odds ratio plots using discrete change the results of ^prchange^
must be in memory. The syntax is:
^mlogplot^ [varlist] [^, or^atio ^dc^hange ^s^td^(^[^s^|^u^|^0^]...[^s^|^u^|^0^]^)^
^min(^#^) max(^#^) packed labels p^rob^(^#^) b^asecategory^(^#^)^
nt^ics^(^#^) n^ote^(^string^)^ ^val^ues ^dcadd(^#^) ^saving(^filename[^,replace^]^)^]
To create odds ratio plots with coefficients that you have placed into
matrices (e.g.,to plot coefficients from a published paper):
^mlogplot,^ ^matrix v^ars^(^varlist^)^ ^s^td^(^[^s^|^u^|^0^]...[^s^|^u^|^0^]^)^
[^min(^#^) max(^#^) packed labels b^asecategory^(^#^) nt^ics^(^#^) n^ote^(^string^)^
^saving(^filename[^,replace^]^)^]
The matrices you must create are discussed below.
Description
-----------
^mlogplot^ takes coefficients either from ^mlogit^ or from other programs that
estimate the multinomial logit model. When estimates from other programs are
used they must be passed to ^mlogplot^ as matrices. ^mlogplot^ then plots the
odds ratios (i.e., exp(b)) and/or the discrete change coefficients. You can
list the same variable more than once if you want both standardized and
unstandardized coefficients to be plotted in the same graph. A variety of
options control the way the final graph looks. The dialog box program ^mlogview^
provides a convenient front end that allows you to use this program
interactively.
^IMPORTANT^: When using coefficients from ^mlogit^, you must be sure that the
value labels for the dependent variable begin with different letters or
numbers. Otherwise, the plot might be misleading.
Options for Plotting Estimates from ^mlogit^
---------------------------------------------
^oratio^ indicates that you want to plot the odds ratios (i.e., exp(b)).
^dchange^ requests plots involving discrete change. To use this option, you
must have first run ^prchange^. This option does not work with the ^matrix^
option.
If ^oratio^ has been specified, this options plots the size of the letters
proportional to the size of the corresponding discrete change coefficient.
See ^dcadd^ and ^dcbase^ for further details.
If ^oratio^ is not specified, ^dchange^ indicates that you want to plot the
discrete change coefficients.
^basecategory^ is used for an odds ratio plot to specify which category of
the outcome measure is to be used as the reference point.
^labels^ uses variable labels to label each row of the plot. You might need to
revise your variable labels to make them fit the graph. This option does
not work with the ^matrix^ option.
^min(^#^) and max(^#^)^ specify the minimum value and the maximum value on which
the coefficients are plotted. This is useful if you want to compare
coefficients from different logits, or produces several plots from the
same logit.
^ntics^ sets the number of tic marks to show on the axes. Used along with ^min^
and ^max^, this allows you to determine the numbering on the axes and the
location of tic marks.
^packed^ removes the vertical spacing among the outcome categories. This allows
up to 11 variables on a single graph. Otherwise, the maximum is 5.
^prob^ is used for an odds ratio plot to specify that if a coefficient
contrasting two outcomes is not signficant at this level, a line is to
be drawn connecting the letters.
^std(^[^s^|^u^|^0^]...[^s^|^u^|^0^]^)^ specifies the type of coefficient to plot. s specifies
standardized coefficients; u specifies unstandardized coefficients; 0
specifies changes from 0 to 1 in discrete change plots. For example,
std(u0su) indicates that the first variable is unstandardized, the second
is binary, the third standardized, and the last is unstandardized.
^dcadd(^#^)^ is rarely used. In odds ratio plots where
the ^dchange^ option is specified, the size of the letter corresponds to
the square root of the size of the discrete change coefficient. ^dcadd^ adds
an amount to each discrete change making the size of all letters larger,
making it easier to see the letters for small discrete changes. By
default this quantity is 0. If your letters print too small, you might
want to increase this by a small amount, say ^dcadd(.03)^.
Plotting Estimates from Matrices
--------------------------------
The ^matrix^ option indicates that estimates are to be obtained from global
matrices, which are described below. When this option is used, the options
^prob^ and ^dchange^ are not allowed. All other options can be used.
^vars(varlist)^ contains the names of the variables whose coefficients you
want to plot. The names must be in the order you want to plot them and
must be included in the global ^mnlname^.
Globals for Plotting Matrices
-----------------------------
^mnlbeta^ contains the betas in a matrix where element (i,j) is the j-th
variable for comparison i relative to the reference category. That is,
columns are for variables; rows for different contrasts. Note that
constants are NOT included.
^mnlname^ contains the names of the variables corresponding to the columns of
mnlbeta.
^mnlcatnm^ is a string with labels for the outcome categories. The
1st category corresponds to the 1st column of mnlbeta, the 2nd to the
2nd, etc. The label for the reference category should be last. Thus,
mnlcatnm provides labels for the rows of mnlbeta. The option
^basecategory^ references which letter in this string represents the
base category.
^mnlrefn^ is the number of the category in mnlcatnm that is the reference
category for the contrasts contained in mnlbeta.
^mnldepnm^ contains name of dependent variable.
^mnlsd^ contains the standard deviations for the variables that correspond
to columns of mnlbeta
Example using mlogit
--------------------
. ^mlogit occ white ed exper,basecategory(5)^
. ^prchange^
. ^mlogplot white ed exper, dc std(0ss) min(-2.75) max(.55)^
Example using matrices
----------------------
. ^matrix mnlsd = (2.946427, 13.95936, 2.946427, 13.95936)^
. ^global mnlname = "W_Educ W_Exper NW_Educ NW_Exper"^
. ^global mnlrefn = 5^
. ^global mnlcatnm = "Menial BlueC Craft WhiteC Prof"^
. ^matrix mnlbeta = (-.83075, -.92255, -.68761, -.41964 \ /*^
> ^*/ -.03380, -.03145, -.00026, .00085 \ /*^
> ^*/ -.70126, -.56070, -.88250, -.53115 \ /*^
> ^*/ -.11084, -.02611, -.15979, -.05209 )^
. ^matrix mnlbeta = mnlbeta'^
. ^mlogplot, vars(W_Educ NW_Educ W_Exper NW_Exper) matrix /*^
> ^*/ std(ssss) note("Effects of Education")
.-
Authors: J. Scott Long and Jeremy Freese
www.indiana.edu/~jsl650/spost.htm
spostsup@@indiana.edu

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{smcl}
{* 20Oct2009 version 1.7.6 jsl}{...}
{cmd:help mlogtest}: Help for tests for the multinomial logit model - 2009-10-20
{hline}
{p2colset 4 14 14 2}{...}
{title:Overview}
{p 4 4 2 78}
The command {cmd:mlogtest} computes a variety of tests for the multinomial logit model.
The user selects the tests they want by specifying the appropriate options. For each
independent variable, {cmd:mlogtest} can perform either a LR or Wald test of the
null hypothesis that the coefficients of the variable equal zero across all equations.
{cmd:mlogtest} can also perform Wald or LR tests of whether any pair of outcome
categories can be combined. In addition, {cmd:mlogtest} computes the Hausman and
Small-Hsiao tests of the assumption of the independence of irrelevance alternatives (IIA)
for each possible omitted category.
{title:Syntax}
{p 8 13 2}
{cmd:mlogtest} [{it:varlist}]
[{cmd:,} {it:options}]
{synoptset 15 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:{it:Tests of variables}}
{synopt:{opt varlist}}Selects variables to test with the wald or lr options.
By default, all variables in the model are tested.{p_end}
{synopt:{opt w:ald}}Use Wald tests for each variable.{p_end}
{synopt:{opt lr}}Use LR test for each variable.{p_end}
{synopt:{opt set}{bf:(}{it:varlist} [{bf:\} {it:varlist}]{bf:)}}
Specify a set of variables is to be tested with {cmd:lrtest} or
{cmd:lr}. The slash {bf:\} specifies multiple sets of variables.
This option is particularly useful when a categorical variable is
included as a set of dummy variables, allowing that the coefficients for
all of the dummy variables are zero across all equations.{p_end}
{syntab:{it:Tests for combining categories}}
{synopt:{opt c:ombine}}Compute Wald tests of whether two outcomes can be
combined.{p_end}
{synopt:{opt lrc:ombine}}Compute LR tests of whether two outcomes can be
combined.{p_end}
{syntab:{it:Tests of IIA}}
{synopt:{opt h:ausman}} Compute Hausman-McFadden tests using Stata's {cmd:hausman}
command.{p_end}
{synopt:{opt d:etail}}Detailed results for {cmd:hausman} option are given.{p_end}
{synopt:{opt sm:hsiao}}Compute Small-Hsiao tests{p_end}
{synopt:{opt su:est}}Compute Hausman-McFadden tests using Stata's {cmd:suest}
command.{p_end}
{synopt:{opt i:ia}}All of the IIA tests should be computed.{p_end}
{synopt:{opt b:ase}}Conduct IIA test omitting the base category of the original
{cmd:mlogit} estimation. This is done by re-estimating the model using the largest
remaining category as the base category. The original estimates are
restored to memory.{p_end}
{syntab:{it:Other}}
{synopt:{opt a:ll}}All tests should be performed.{p_end}
{synoptline}
{title:Examples}
{bf: . mlogit whoclass income dad_educ male black hispanic asian}
{bf: . * compute all tests}
{bf: . mlogtest, all}
{bf: . mlogit whoclass income dad_educ male black hispanic asian singlpar}
{bf: > stepmommlogit whoclass income dad_educ male black hispanic asian}
{bf: . * teset groups of dummy variables}
{bf: . mlogtest, lr set(black hispanic asian \ singlpar stepmom stepdad)}
{title:Returned matrices}
{p 4 4}
{bf:r(combine)}: results of Wald tests to combine categories. Rows represent all
contrasts among categories; columns indicates the categories contrasted, the
chisq, df, and p of test.
{p 4 4}
{bf:r(lrcomb)}: results of LR tests to combine categories. Rows represent all
contrasts among categories; columns indicates the categories contrasted, the
chisq, df, and p of test.
{p 4 4}
{bf:r(hausman)}: results of Hausman tests of IIA assumption. Each row is one test.
Columns indicate the omitted category of a given test, the chisq, df, and p.
{p 4 4}
{bf:r(smhsiao)}: results of Small-Hsiao tests of IIA assumption.
{p 4 4}
{bf:r(wald)}: results of Wald test that all coefficients of an independent variable
equals zero
{p 4 4}
{bf:r(lrtest)}: results of likelihood-ratio test that all coefficients of an
independent variable equals zero
{title:Acknowledgment}
{p 4 4}
The code used for the Small-Hsiao test is based on a program by Nick Winter.
INCLUDE help spost_footer

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*! version 2.5.0 2009-10-28 jsl
* - stata 11 update for returns from -mlogit-
window control clear
capture program drop mlogview
capture program drop _mlgetv
capture program drop _mlinit
capture program drop _mlplot
capture program drop _mlnxt7
capture program drop _mlprnt
capture program drop _mlhlp
program define mlogview
if "`e(cmd)'"!="mlogit" {
di in r "mlogview must be run after mlogit"
exit
}
version 6.0
*=> Initialize
global mlopts "`*'"
_mlinit
global ml_lbl ""
*=> List variables to plot
local r1 = 2
local c1 = 5
local d1 = 12
* c's are columns for radio buttons
local c2 = `c1' + 55
local c3 = `c2' + 20
local c4 = `c3' + 28
local c5 = `c4' + 24
* 18Nov2005 - add button for range
local c6 = `c5' + 36
* global ml_wi "Select Variables Select Amount of Change"
* window control static ml_wi `c1' `r1' 146 9
global ml_wi "Select Variables"
window control static ml_wi `c1' `r1' 146 9
global ml_wi2 "Select Amount of Change"
local c12 = `c1' + 56
window control static ml_wi2 `c12' `r1' 146 9
local r1 = `r1' + `d1' - 1
* var#1
local n1 = 1
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005 - add Range option
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
local r1 = `r1' + `d1'
* var#2
local n1 = 2
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
local r1 = `r1' + `d1'
* var#3
local n1 = 3
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
local r1 = `r1' + `d1'
* var#4
local n1 = 4
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
local r1 = `r1' + `d1'
* var#5
local n1 = 5
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
* var#6
local r1 = `r1' + `d1'
local n1 = 6
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
* 18Nov2005
* window control radend "Don't Plot" `c5' `r1' 40 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
*> 18Nov2005 - Add 7th variable
* var#7
local r1 = `r1' + `d1'
local n1 = 7
window control scombo ml_rhsnm `c1' `r1' 50 50 ml_v`n1'
window control radbegin "+1" `c2' `r1' 20 9 ml_v`n1'r
window control radio "+SD" `c3' `r1' 24 9 ml_v`n1'r
window control radio "0/1" `c4' `r1' 24 9 ml_v`n1'r
window control radio "Range" `c5' `r1' 32 9 ml_v`n1'r
window control radend "Don't Plot" `c6' `r1' 40 9 ml_v`n1'r
*<
*=> Define buttons that execute the plot program
local r1 = `r1' + `d1'
/* 18Nov2005 - change size
window control button "DC Plot" 2 `r1' 40 13 ml_dc
global ml_dc "_mlplot 1"
window control button "OR Plot" 46 `r1' 40 13 ml_or
global ml_or "_mlplot 2"
window control button "OR+DC Plot" 90 `r1' 40 13 ml_od
global ml_od "_mlplot 3"
window control button "Next 6" 134 `r1' 40 13 ml_nxt7
global ml_nxt7 "_mlnxt7"
*/
local w = 52
local w1 = `w' + 4
local w2 = `w' + `w' + 4
local w3 = `w' + `w' + `w' + 4
window control button "DC Plot" 2 `r1' `w' 13 ml_dc
global ml_dc "_mlplot 1"
window control button "OR Plot" `w1' `r1' `w' 13 ml_or
global ml_or "_mlplot 2"
window control button "OR+DC Plot" `w2' `r1' `w' 13 ml_od
global ml_od "_mlplot 3"
window control button "Next 7" `w3' `r1' `w' 13 ml_nxt7
global ml_nxt7 "_mlnxt7"
*=> Add a note to graph
local r1 = `r1' + `d1' + 7
local r2 = `r1'
global ml_wlbl "Note"
window control static ml_wlbl 5 `r1' 18 9
window control edit 25 `r2' 148 8 ml_lbl
*=> Plot options
local r1 = `r1' + `d1' + 3
global ml_opt "Plot Options"
* window control static ml_opt 2 `r1' 173 40 blackframe
* version 1.6.5
* window control static ml_opt 2 `r1' 173 50 blackframe
* 17Nov2005
window control static ml_opt 2 `r1' 210 60 blackframe
local r1 = `r1' - 3
window control static ml_opt 5 `r1' 40 8
local r1 = `r1' + `d1' - 3
local r2 = `r1'
* tics
global ml_wtic "Number of tics"
window control static ml_wtic 5 `r1' 47 8
window control edit 50 `r2' 25 8 ml_tic
* range of plot
global ml_wmin "Plot from"
window control static ml_wmin 90 `r1' 28 8
window control edit 120 `r1' 17 8 ml_xmin
global ml_wmax "to"
window control static ml_wmax 140 `r1' 6 8
window control edit 149 `r1' 17 8 ml_xmax
* connect if p>
local r1 = `r1' + `d1'
local r2 = `r1'
global ml_wpgt "Connect if p>="
window control static ml_wpgt 5 `r1' 42 8
window control edit 50 `r2' 25 8 ml_pval
* set base category
global ml_wb "Base category"
window control static ml_wb 90 `r1' 45 8
window control edit 137 `r2' 28 8 ml_bcat
* pack odds and use variable labels
local r1 = `r1' + `d1' - 2
window control check "Pack odds ratio plot" 5 `r1' 80 10 ml_pack
window control check "Use variable labels" 90 `r1' 75 10 ml_vlbl
* pack odds and use variable labels
local r1 = `r1' + `d1' - 2
window control check "Use category values for plot symbols" 5 `r1' 180 10 ml_valu
* 17Nov2005 - add underline option for OR+DC plots
local r1 = `r1' + `d1' - 2
window control check "Underline indicates negative change" 5 `r1' 180 10 ml_under
*=> Buttons for odds and ends
local r1 = `r1' + 15
* 18Nov2005 - drop help button - no longer works
* window control button "Help" 46 `r1' 40 13 ml_help
* global ml_help "_mlhlp"
* 18Nov2005
* window control button "Exit" 2 `r1' 40 13 ml_ex
* window control button "Print" 90 `r1' 83 13 ml_print
local w = 103
local wx = `w' + 5
window control button "Exit" 2 `r1' `w' 13 ml_ex
global ml_ex "exit 3000"
window control button "Print" `wx' `r1' `w' 13 ml_print
global ml_print "_mlprnt"
* window dialog "Multinomial Logit Plots" 10 10 180 192
* window dialog "Multinomial Logit Plots" 230 80 180 192
* version 1.6.5
*window dialog "Multinomial Logit Plots" 230 80 180 202
* 17Nov2005
*window dialog "Multinomial Logit Plots" 10 10 320 302
* x y
*window dialog "Multinomial Logit Plots" 10 10 220 302
window dialog "Multinomial Logit Plots" 10 10 220 225
end
program define _mlplot
if "`1'" ~= "" {
global ml_ordc = `1'
}
*=> construct list of vars from the scombo boxes 1 through 6
*=> construct list of vars from the scombo boxes 1 through 7
* 18Nov2005
local i = 1
local varlst ""
local stdlst ""
while `i' < 8 {
* while `i' < 7 {
* type of plot: unstd, std, or 0 to 1
local tmp "ml_v`i'r"
* name of variable
local tmpnm "ml_v`i'"
if $`tmp'==1 {
local varlst "`varlst' $`tmpnm'"
local stdlst "`stdlst'u"
}
if $`tmp'==2 {
local varlst "`varlst' $`tmpnm'"
local stdlst "`stdlst's"
}
if $`tmp'==3 {
local varlst "`varlst' $`tmpnm'"
local stdlst "`stdlst'0"
}
* 18Nov2005
if $`tmp'==4 {
local varlst "`varlst' $`tmpnm'"
local stdlst "`stdlst'r"
}
* 18Nov2005
* if type is not 1, 2, 3, or 4 it is not plotted
* if type is not 1, 2, or 3, it is not plotted
local i = `i' + 1
}
*=> build options to pass to mlogplot
local opts "std(`stdlst')"
if "$ml_bcat"~="" {
local opts "`opts' b($ml_bcat)"
}
local opts "`opts' p($ml_pval)"
if "$ml_xmin"~="min" {
local opts "`opts' min($ml_xmin)"
}
if "$ml_xmax"~="max" {
local opts "`opts' max($ml_xmax)"
}
if "$ml_lbl" ~= "" {
local opts "`opts' note($ml_lbl)"
}
if $ml_ordc>=2 {
local opts "`opts' or"
}
if $ml_ordc~=2 {
local opts "`opts' dc"
}
if $ml_pack==1 {
local opts "`opts' packed"
}
if $ml_vlbl==1 {
local opts "`opts' labels"
}
* 1.6.5
if $ml_valu==1 {
local opts "`opts' values"
}
* 18Nov2005
if $ml_under==1 {
local opts "`opts' sign"
}
local opts "`opts' ntics($ml_tic)"
local opts "`opts' $mlopts"
di in white ". mlogplot `varlst', `opts'"
mlogplot `varlst',`opts'
* if $PE_mlerr==1 {
* exit 3000
* }
end
program define _mlinit
*=> set radio buttons
local i = 1
while `i' < 6 {
global ml_v`i'r = 1
local i = `i' + 1
}
*=> defaults for check box
global ml_rng = 1 /* use observed range */
global ml_ordc = 1 /* DC plot */
global ml_pack = 0 /* don't pack plot */
global ml_vlbl = 0 /* don't plot value labels */
* 17Nov2005
global ml_under = 0 /* underline negative changes */
* 1.6.5
global ml_valu = 0 /* don't plot value labels */
global ml_pval = .1 /* connect if p> */
global ml_tic = 9
global ml_lbl ""
global ml_xmin "min" /* plot from min to max */
global ml_xmax "max"
*=> get b from logit
/* 2009-10-28
version 5.0
mat ml_b = get(_b)
version 6.0
*/
tempname v
_get_mlogit_bv ml_b `v'
global ml_nvars = colsof(ml_b) - 1
*=> get names of variables
global ml_rhsnm : colnames(ml_b)
global ml_nvar : word count $ml_rhsnm
global ml_nvar = $ml_nvar - 1
global ml_lastv = 1
global ml_lastv = 0
_mlgetv
end
* get the names of variables to fill in the scombo boxes
program define _mlgetv
* get number of last variable in box; 0 if none plotted before.
* if this is called from Next 6, this will not be 0.
local k = $ml_lastv
local i = 1
* 18Nov2005
* loop through up to 6 new variables
* while `i' < 7 {
* loop through up to 7 new variables
while `i' < 8 {
local k = `k' + 1
* if exceed max number of vars, do fill remaining scombo boxes
if `k' > $ml_nvar {
* do not plot
*18Nov2005
* global ml_v`i'r = 4
global ml_v`i'r = 5
* no name
global ml_v`i' ""
}
* else, get next in list of variables
else {
global ml_v`i' : word `k' of $ml_rhsnm
local tmp "ml_v`i'"
_pedum $`tmp'
global ml_v`i'r = 1
if r(dummy) == 1 { global ml_v`i'r = 3 }
}
local i = `i' + 1
}
* ok that this can be larger than n vars in model
* 18Nov2005
*global ml_lastv = $ml_lastv + 6
global ml_lastv = $ml_lastv + 7
end
program define _mlnxt7
* if last var from _mlgetv > n vars in model, reset
if $ml_lastv > $ml_nvar {
global ml_lastv = 0
}
_mlgetv
_mlplot
end
program define _mlprnt
gphprint,nologo
end
exit
* version 1.6.3 11Mar2001
* version 1.6.4 19Nov2003 - for stata 8, change where box opens
* version 1.6.5 30Mar2005 plot values not labels
* version 1.6.6 13Apr2005 plot values not labels
* version 1.7.0 18Nov2005 add sign and more variables - mlogview
* version 1.7.1 01Apr2006
* - fix dialog box

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.-
help for ^mlogview^ - 1.6.1 - 2/22/01
.-
Plots odds ratios and discrete change coefficients from the
multinomial logit model.
------------------------------------------------------------
^Select variables^: select independent variables to include in plot
^Select amount of change^: amount of discrete change to plot for each variable
^+1^: unit change
^+SD^: standard deviation change
^0->1^: change from 0 to 1
^Don't plot^: exclude variable from plot
(Starting values for discrete change taken from output from ^prchange^, which
must be executed prior to executing ^mlogview^ if discrete change is to be
plotted.)
^DC Plot^: draw discrete change plot
^OR Plot^: draw odds ratio plot
^OR+DC Plot^: draw odds ratio plot in which the size of the letters
indicates the discrete change
^Next 6^: list next 6 independent variables for packed plot
^Note^: title for plot
^Number of tics^: # of tic marks on x axis of plot
^Plot from^: minimum and maximum values of x axis for plot
^Connect if^: for odds ratio plots, connect lines if not significant at
specified level
^Base category^: value of category to use as base category for odds ratio plot
^Pack odds ratio plot^: eliminate extra vertical space in odds ratio plot
^Use variable labels^: use variable labels instead of names to identify
variables in plot
^Print^: send plot to printer
.-
Author: J. Scott Long
www.indiana.edu/~jslsoc/spost.htm
spostsup@@indiana.edu

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program def mnthlist, rclass
*! NJC 1.0.0 9 January 2001
version 6.0
#delimit ;
syntax , [ Global(str) Noisily Caplc Uppercase Long
Number(int 12) Begin(str) Step(numlist int max=1 >=1)
Year(numlist int max=1 >100 <10000) ] ;
#delimit cr
if length("`global'") > 8 {
di in r "global name must be <=8 characters"
exit 198
}
if "`caplc'" != "" & "`uppercase'" != "" {
di in r "must choose between cap/lc and uppercase"
exit 198
}
if "`begin'" == "" {
local begin 1
}
else {
capture confirm integer number `begin'
if _rc == 0 {
if `begin' < 1 | `begin' > 12 {
di in r "invalid begin( ) option"
exit 198
}
}
else { /* grind through possible abbreviations */
local b = lower(trim("`begin'"))
if substr("`b'",1,2) == "ja" {
local begin 1
}
else if substr("`b'",1,1) == "f" {
local begin 2
}
else if substr("`b'",1,3) == "mar" {
local begin 3
}
else if substr("`b'",1,2) == "ap" {
local begin 4
}
else if substr("`b'",1,3) == "may" {
local begin 5
}
else if substr("`b'",1,3) == "jun" {
local begin 6
}
else if substr("`b'",1,3) == "jul" {
local begin 7
}
else if substr("`b'",1,2) == "au" {
local begin 8
}
else if substr("`b'",1,1) == "s" {
local begin 9
}
else if substr("`b'",1,1) == "o" {
local begin 10
}
else if substr("`b'",1,1) == "n" {
local begin 11
}
else if substr("`b'",1,1) == "d" {
local begin 12
}
else {
di in r "invalid begin( ) option"
exit 198
}
}
}
if "`step'" == "" { local step = 1 }
* upper( ) and lower( ) won't take strings >80 chars
if "`long'" != "" {
local m1 "January February March April May June"
local m2 "July August September October November December"
}
else {
local m1 "Jan Feb Mar Apr May Jun"
local m2 "Jul Aug Sep Oct Nov Dec"
}
if "`caplc'" == "" {
local m1 = lower("`m1'")
local m2 = lower("`m2'")
}
if "`uppercase'" != "" {
local m1 = upper("`m1'")
local m2 = upper("`m2'")
}
local m "`m1' `m2'"
local i = 1
local j = `begin'
if "`year'" != "" {
local y = `year' + (`begin' - 0.5) / 12
}
while `i' <= `number' {
local mnth : word `j' of `m'
local newlist "`newlist'`mnth'`year' "
local j = mod(`j' + `step', 12)
local j = cond(`j' == 0, 12, `j')
if "`year'" != "" {
local y = `y' + `step' / 12
local year = int(`y')
}
local i = `i' + 1
}
if "`noisily'" != "" { di "`newlist'" }
if "`global'" != "" { global `global' "`newlist'" }
return local list `newlist'
end

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.h listutil

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*! version 1.0.0 \ scott long 2007-08-05
// task: tabulate only missing values
// project: workflow chapter 4
// author: scott long \ 2007-08-05
// based on tab1.ado version 2.2.4 29sep2004 by StataCorp
program define mvtab1, byable(recall)
version 6, missing
syntax varlist [if] [in] [fweight] [, *]
tokenize `varlist'
local stop : word count `varlist'
local i 1
tempvar touse
mark `touse' `if' `in' [`weight'`exp']
local weight "[`weight'`exp']"
capture {
while `i' <= `stop' {
noisily di _n `"-> tabulation of ``i'' `if' `in'"'
* VVVVVVVV VVVV
cap noisily tab ``i'' if `touse' & ``i''>=. `weight' , `options' miss
if _rc!=0 & _rc!=1001 { exit _rc }
local i = `i' + 1
}
}
error _rc
end
exit

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.-
help for ^mvtab1^ :: 2008-03-07
.-
Tabulate missing values
-----------------------
^mvtab1^ varlist^,^ (see options for ^tab1^)
Description
-----------
^mvtab1^ operates just like ^tab1^ except that it only prints frequency
distributions for missing values.
Options
-------
See ^tab1^ for a listing of options.
.-
Author: Scott Long - www.indiana.edu/~jslsoc/workflow.htm