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Activity Number: 495
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #320087 View Presentation
Title: Multidimensional Latent Trait Linear Mixed Model with an Application in Clinical Trials
Author(s): Jue Wang* and Sheng Luo
Companies: The University of Texas Health Science Center at Houston and The University of Texas at Houston
Keywords: clinicial trial ; multidimensional ; latent trait model ; MCMC ; random effects

Multilevel item response theory (MLIRT) models have been widely used to analyze the multivariate longitudinal data of mixed types (e.g., categorical and continuous) in clinical studies. Unidimensionality is assumed in MLIRT models that there exists a single latent variable to measure the disease severity. However, there may be multiple latent variables representing multidimensional impairment caused by certain diseases. In this article, we propose a multidimensional latent trait linear mixed model (MLTLMM) that allows multiple latent variables and within-item multidimensionality, which means one outcome can be a manifestation of more than one latent variable. We conduct extensive simulations studies to assess and compare the performance of unidimensional and multidimensional latent trait models under various scenarios. The simulation studies suggest that the multidimensional model outperforms unidimensional model when the multivariate longitudinal outcomes are related to multiple latent variables. The proposed model is applied to the motivating clinical trial of ceftriaxone in subjects with Amyotrophic Lateral Sclerosis (ALS).

Authors who are presenting talks have a * after their name.

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