title: Bayesian Variable Selection under the Proportional Hazards Mixed-effects Model.
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. A stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM) is developed. 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.
authors.affiliation: Department of Statistics, Kyungpook National University, Daegu, 702-701, Korea.
authors.forename: Kyeong Eun
authors.initials: KE
authors.lastname: Lee
authors.affiliation: Department of Statistics, Kyungpook National University, Daegu, 702-701, Korea.
authors.forename: Yongku
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine and Department of Mathematics, University of California, San Diego, USA.
authors.forename: Ronghui
authors.initials: R
authors.lastname: Xu
keywords: MCMC; correlated survival data; model selection; multi-center clinical trial; proportional hazards mixed-effects model; stochastic search variable selection