Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study

Tolulope T.Sajobi; Lisa M.Lix; LaraRussellDavidSchulzJuxinLiuBruno D.ZumboRichardSawatzky

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Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study

机译：Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study

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Abstract Purpose Mixture item response theory (MixIRT) models can be used to uncover heterogeneity in responses to items that comprise patient-reported outcome measures (PROMs). This is accomplished by identifying relatively homogenous latent subgroups in heterogeneous populations. Misspecification of the number of latent subgroups may affect model accuracy. This study evaluated the impact of specifying too many latent subgroups on the accuracy of MixIRT models.Methods Monte Carlo methods were used to assess MixIRT accuracy. Simulation conditions included number of items and latent classes, class size ratio, sample size, number of non-invariant items, and magnitude of between-class difference in item parameters. Bias and mean square error in item parameters and accuracy of latent class recovery were assessed.Results When the number of latent classes was correctly specified, the average bias and MSE in model parameters decreased as the number of items and latent classes increased, but specification of too many latent classes resulted in modest decrease (i.e.,?

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《Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation》 |2022年第12期|3423-3432|共10页
作者
Tolulope T.Sajobi; Lisa M.Lix; LaraRussellDavidSchulzJuxinLiuBruno D.ZumboRichardSawatzky;
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作者单位

Department of Community Health Sciences,University of Calgary;

Department of Community Health Sciences,University of Manitoba;

Faculty of Nursing,Trinity Western UniversityResearch Computing Services,University of CalgaryDepartment of Mathematics & Statistics,University of SaskatchewanDepartment of Educational and Counselling, Psychology, and Special Education, (MERM Program;

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原文格式 PDF
正文语种英语
中图分类医药、卫生;
关键词
Patient-reported outcomes measures; Latent class; Item response theory; Measurement invariance; Computer simulation;

Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study

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