Bayesian variable selection for the Cox regression model with missing covariates

被引：0
|
作者：
Joseph G. Ibrahim
Ming-Hui Chen
Sungduk Kim
机构：
[1] University of North Carolina,Department of Biostatistics
[2] University of Connecticut,Department of Statistics
[3] National Institute of Child Health and Human Development,Division of Epidemiology, Statistics and Prevention Research
[4] NIH,undefined
来源：
Lifetime Data Analysis | 2008年 / 14卷
关键词：
Conjugate prior; Deviance information criterion; Missing at random; Proportional hazards models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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收藏
页码：496 / 520
页数：24
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