Evaluation of covariate effects in item response theory models

Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item‐specific effects. Finding subgroups of patients who respond differently to certain items could...

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Bibliographic Details
Published inCPT: pharmacometrics and systems pharmacology Vol. 13; no. 5; pp. 812 - 822
Main Authors Wellhagen, Gustaf J., Yassen, Ashraf, Garmann, Dirk, Bröker, Astrid, Solms, Alexander, Zhang, Yang, Kjellsson, Maria C., Karlsson, Mats O.
Format Journal Article
LanguageEnglish
Published United States John Wiley & Sons, Inc 01.05.2024
John Wiley and Sons Inc
Wiley
Subjects
Computer Simulation
Disease
Estimates
Humans
Item response theory
Models, Statistical
Probability
Simulation
Womens health
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Summary:Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item‐specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations. We present a new method to investigate item‐specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.
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ISSN:2163-8306
2163-8306
DOI:10.1002/psp4.13120