Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease.

摘要: 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 outcome-dependent terminal event, e.g. death and dropout. this article, we develop a joint model that consists multilevel item response theory (MLIRT) for outcomes, Cox’s proportional hazard with piecewise constant baseline hazards event time data. Shared random effects used link together two models. inference is conducted using Bayesian framework via Markov Chain Monte Carlo simulation implemented BUGS language. Our proposed evaluated studies applied DATATOP study, motivating trial assessing effect tocopherol on PD among early PD.