A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.

ABSTRACT: Impairment caused by Parkinson's disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of th

SUBMITTER: Luo S 

PROVIDER: S-EPMC3947121 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature