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Lifetime Data Analysis

, 14:496 | Cite as

Bayesian variable selection for the Cox regression model with missing covariates

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Sungduk Kim
Article

Abstract

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.

Keywords

Conjugate prior Deviance information criterion Missing at random Proportional hazards models 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Sungduk Kim
    • 3
  1. 1.Department of BiostatisticsUniversity of North CarolinaChapel HillUSA
  2. 2.Department of StatisticsUniversity of ConnecticutStorrsUSA
  3. 3.Division of Epidemiology, Statistics and Prevention ResearchNational Institute of Child Health and Human Development, NIHRockvilleUSA

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