Bayesian Variable Selection Under the Proportional Hazards Mixed-Effects Model

• Activity Number: 553
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Ronghui Xu*+ and Kyeong Eun Lee and Yongku Kim
• Companies: University of California at San Diego and Kyungpook National University and Yoengnam University
• Keywords: correlated survival data ; MCMC ; model selection ; multi-center clinical trial ; proportional hazards mixed-effects model ; stochastic search variable selection

Abstract

Over the past decade much statistical research has been carried out to develop models for correlated survival data; however, methods for model selection are still very limited. In this paper we develop a stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM). The SSVS method has previously been applied to linear and generalized linear mixed models, and to the proportional hazards model with high dimensional data. Because the method has mainly been developed for hierarchical normal mixture distributions, it operates on the linear predictor under the Cox type models. The PHMM naturally incorporates the normal distribution via the random effects, which enables SSVS to efficiently search through the candidate variable space. The approach was evaluated through simulation, and applied to a multi-center lung cancer clinical trial data set, for which the variable selection problem was previously debated upon in the literature.