A Joint Model for Longitudinal Measurements and Competing Risks Survival Data

• Activity Number: 26
• Type: Topic Contributed
• Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Gang Li*+
• Companies: University of California, Los Angeles
• Keywords: Robust model ; Joint model ; Competing risks ; Bayesian method ; Mixed effects model

Abstract

We study a joint model for longitudinal and competing risks survival data. The variance-covariance matrix of the latent random effects is also modeled jointly via a modified Cholesky decomposition. An MCMC procedure is developed for parameter estimation and inference. Our method is robust to outlying longitudinal measurements and allows nonignorable missing data due to dropout. It also provides a framework for handling high-dimensional heterogeneous random effects and testing the homogeneous random effects assumption which is otherwise untestable in commonly used joint models. Finally, our model enables analysis of a survival outcome with intermittently measured time-dependent covariates and possibly correlated competing risks and dependent censoring, as well as joint analysis of the longitudinal and survival outcomes. Illustrations are given using a real data set and simulations.