期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 25, 期 4, 页码 1346-1358出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280213480877
关键词
joint model; item-response theory; latent variable; Markov Chain Monte Carlo; mixed model
类别
资金
- NIH/NINDS [U01NS043127, U01NS43128]
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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