help for rosali22Myriam Blanchin - Priscilla Brisson

Detection of Response Shift at Item-Level between 2 times

rosali22 varlist [if] [in] [, id(string)
moda(# # [#]...) group(string) nodif]

varlist contains items' list : the first half of the items represents the items at time 1 and the second half the items at time 2. (at least 2 items).

Data : wide format, one line for one patient

Description

rosali22: RespOnse Shift ALgorithm at Item-level (ROSALI),
detection of Response-Shift between two measuring times based on partial credit model.
Only uniform or non-uniform recalibration (RC) can be detected.
A dichotomous group covariate can affect the estimation of the true change(group effect)
and of response shift detected.
Response shift can be common to both groups or differential.
Detection of differential item functionning (DIF) between two groups with the option group.

PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT FIRST TIME OF MEASUREMENT

Only with group option and without nodif option

Model A : Full model, transversal PCM : estimates of item difficulties free between groups, estimates of latent trait with group effect constrained to 0.

Model B : Restricted model, transversal PCM : estimates of item difficulties with constraint of equality between groups, estimates of latent trait including group effect estimate.

.LR test between model A and model B. If this test is significant, algorithm proceeds to step C, otherwise algorithm proceeds to part 2, with constraint of equality between groups of item difficulties.

Step C : an iterative step to detect which items have different item difficulties between groups at time 1. At each iteration, equality constraint of item difficulties between groups are relaxed one-by-one
producing multiple models C (starting with model B). For each item, difference in item difficulties between groups is tested, a Bonferroni correction is applied. Item with the most significant test is selected and tested
to determine if the difference in item difficulties is uniform or non-uniform (if number of answer categories is greater to 2). Model C is updated and step C is repeated to identify differences on the remaining items.

.When there is no more item with significant test or only one remaining item to be tested, ROSALI goes on part 2.

PART 2: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN TIMES (RECALIBRATION)

For each model, algorithm takes account detection of part 1.

Model 1 : Full model, longitudinal PCM : estimates of item difficulties free across times for each group, estimates of latent trait with time effect and interaction time x group constrained to 0.

Model 2 : Restricted model, longitudinal PCM : estimates of item difficulties with constraint of equality across times, estimates of latent trait including time effect and interaction time x group estimate.

.LR test between model 1 and model 2. If this test is significant, algorithm proceeds to step 3, otherwise algorithm proceeds to step 4, keeping model 2.

Step 3 : an iterative step to detect which items have different item difficulties across times. At each iteration, equality constraint of item difficulties between times are relaxed one-by-one producing multiple
models 3 (starting with model 2). For each item, recalibration is tested, a Bonferroni correction is applied. Item with the most significant test is selected and tested.

.If group option, item is tested to determine if the difference in item difficulties is the same for each group (common RC), in this case, model 3 is updated to take account common recalibration, or different
(differential RC), in this case, recalibration is tested for each group with a Bonferroni correction.

.Type of recalibration : uniform or non-uniform, is finally tested (if number of answer categories is greater to 2). Model 3 is updated and step 3 is repeated to identify differences on the remaining items.

.When there is no more item with significant test or only one remaining item to be tested, ROSALI goes on part 4.

Model 4 : Item difficulties and latent trait are estimated with a longitudinal PCM taking account difference or no between groups or times and the type uniform or non-uniform. If interaction time x group is
not significant model 4 is updated with constraint of interaction equal to 0 and we obtained final model.

automatic coding for answers categories: Answers at items must be respect 2 conditions. If it is necessary algorithm recodes answers automatically. You can recodes answers before.

- Answers must be ordered and start with 0. If it's necesary, algorithm recodes automatically answers categories to the first answer is 0.

- It's necessary that all answers categories are used at two times of measurement (for each group if option was used).

.If the first answer is not used at one time (or in a group) at least: this answer is merged with next answer.

.If the last answer is not used at one time (or in a group) at least: this answer is merged with the previous answer.

.If an intermediate answer is not used at one time (or in one group) at least: this answer is merged with the next or previous answer of randomly way.

examples : If there is 4 answers categories, answers lightly are : 0, 1, 2 or 3.

- If answer 0 is not used at one time or for one group, answers 0 and 1 merged (patient who had answered 0 or 1 have now the answer 0, the answer 2 became answer 1 and answer 3 became answer 2)

- If answer 3 is not used at one time or for one group, answers 2 and 3 merged (patient who had answered 2 or 3 have now the answer 2, answers 0 and 1 remain the same)

- If answer 1 (resp. 2) is not used at one time or for one group, randomly answer 1 (resp. 2) merged with answer 0 (resp. 1) or with answer 2 (resp. 3).

automatic coding for groups: Rosali needs to have one group 0 and one group 1. If it's necessary Rosali recodes group variable. You can recodes groups before.

Options

id(string) specifies the identifiant of individuals. Necessary to validate the data format.

moda(# # [#]...) specifies the number of answers categories for each item.

group(string) specifies the binary group variable, allows the detection of differential item functionning (DIF).

nodif specifies to do only the part 2 of algorithm. No detection of DIF, only detection of response shift for each group. Use only with group option.

Outputs

Matrix:

r(test_model): Result of LRT between models A/B and models 1/2: chi-square, DF and p-value.

r(model_#): Item difficulties and latent trait of model 2 and 4 : Estimates, standard error, confidence interval at 95%, chi-square, DF and p-value.

Examples

. rosali22 itemA1 itemA2 itemA3 itemB1 itemB2 itemB3, id(mat) // 3 items : A = time1 & B = time2

. rosali22 it1_t1-it9_t2 , id(idpat) moda(4 4 7 7 7 7 7 7 7) group(type_c) // 9 items, 4 answers for two first items and seven for others, detection by group of type_c

Authors

Myriam Blanchin, Research engineer, PhD, SPHERE - UMR INSERM U1246, "methodS in Patient-centered outomes and HEalth ResEarch", University of Nantes, France myriam.blanchin@univ-nantes.fr

Priscilla Brisson, SPHERE - UMR INSERM U1246, "methodS in Patient-centered outomes and HEalth ResEarch", University of Nantes, France
priscilla.brisson@univ-nantes.fr