Bayesian variable selection for the Cox regression model with missing covariates.
In this paper, we develop Bayesian methodology and computational algorithms for variable subset selection in Cox proportional hazards models with missing covariate data. A new joint semi-conjugate prior for the piecewise exponential model is proposed in the presence of missing covariates and its properties are examined. The covariates are assumed to be missing at random (MAR). Under this new prior, a version of the Deviance Information Criterion (DIC) is proposed for Bayesian variable subset selection in the presence of missing covariates. Monte Carlo methods are developed for computing the DICs for all possible subset models in the model space. A Bone Marrow Transplant (BMT) dataset is used to illustrate the proposed methodology.
Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. ibrahim@bios.unc.edu
Joseph G
JG
Ibrahim
Ming-Hui
MH
Chen
Sungduk
S
Kim
D000465:Algorithms; D001499:Bayes Theorem; D016026:Bone Marrow Transplantation / Q000401:mortality*; D003627:Data Interpretation, Statistical; D006801:Humans; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016019:Survival Analysis