Bayesian variable selection for the Cox regression model with missing covariates

被引：13

作者：

Ibrahim, Joseph G. ^{[1
]}

Chen, Ming-Hui ^{[2
]}

Kim, Sungduk ^{[3
]}

机构：

[1] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA

[2] Univ Connecticut, Dept Stat, Storrs, CT 06269 USA

[3] NICHHD, Div Epidemiol Stat & Prevent Res, NIH, Rockville, MD 20852 USA

来源：

LIFETIME DATA ANALYSIS | 2008年 / 14卷 / 04期

关键词：

Conjugate prior; Deviance information criterion; Missing at random; Proportional hazards models;

D O I：

10.1007/s10985-008-9101-5

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

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.

引用

页码：496 / 520

页数：25

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