Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 25, Issue 4, Pages 1346-1358Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280213480877
Keywords
joint model; item-response theory; latent variable; Markov Chain Monte Carlo; mixed model
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Funding
- NIH/NINDS [U01NS043127, U01NS43128]
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In many clinical trials, studying neurodegenerative diseases including Parkinson's disease (PD), multiple longitudinal outcomes are collected in order to fully explore the multidimensional impairment caused by these diseases. The follow-up of some patients can be stopped by some outcome-dependent terminal event, e.g. death and dropout. In this article, we develop a joint model that consists of a multilevel item response theory (MLIRT) model for the multiple longitudinal outcomes, and a Cox's proportional hazard model with piecewise constant baseline hazards for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in BUGS language. Our proposed model is evaluated by simulation studies and is applied to the DATATOP study, a motivating clinical trial assessing the effect of tocopherol on PD among patients with early PD.
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