Tolulope T Sajobi1, Lisa M Lix2, Lara Russell3, David Schulz4, Juxin Liu5, Bruno D Zumbo6, Richard Sawatzky3. 1. Department of Community Health Sciences, University of Calgary, Calgary, Canada. tolu.sajobi@ucalgary.ca. 2. Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada. 3. Faculty of Nursing, Trinity Western University, Langley, BC, Canada. 4. Research Computing Services, University of Calgary, Calgary, Canada. 5. Department of Mathematics & Statistics, University of Saskatchewan, Saskatoon, Canada. 6. Department of Educational and Counselling, Psychology, and Special Education, (MERM Program), University of British Columbia, Vancouver, Canada.
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., < 10%) in the accuracy of latent class recovery. CONCLUSION: The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection.
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., < 10%) in the accuracy of latent class recovery. CONCLUSION: The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection.
Authors: Xiuyun Wu; Richard Sawatzky; Wilma Hopman; Nancy Mayo; Tolulope T Sajobi; Juxin Liu; Jerilynn Prior; Alexandra Papaioannou; Robert G Josse; Tanveer Towheed; K Shawn Davison; Lisa M Lix Journal: Health Qual Life Outcomes Date: 2017-05-15 Impact factor: 3.186
Authors: N Black; L Burke; C B Forrest; U H Ravens Sieberer; S Ahmed; J M Valderas; S J Bartlett; J Alonso Journal: Qual Life Res Date: 2015-11-13 Impact factor: 4.147