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

共 50 条

[21] VARIABLE SELECTION IN NONPARAMETRIC REGRESSION WITH CONTINUOUS COVARIATES
ZHANG, P
[J]. ANNALS OF STATISTICS, 1991, 19 (04): : 1869 - 1882
[22] Model uncertainty and variable selection in Bayesian lasso regression
Hans, Chris
[J]. STATISTICS AND COMPUTING, 2010, 20 (02) : 221 - 229
[23] Missing covariates in logistic regression, estimation and distribution selection
Consentino, Fabrizio
Claeskens, Gerda
[J]. STATISTICAL MODELLING, 2011, 11 (02) : 159 - 183
[24] VARIABLE SELECTION IN NONPARAMETRIC REGRESSION WITH CATEGORICAL COVARIATES
BICKEL, P
PING, Z
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1992, 87 (417) : 90 - 97
[25] On functional misspecification of covariates in the Cox regression model
Gerds, TA
Schumacher, M
[J]. BIOMETRIKA, 2001, 88 (02) : 572 - 580
[26] Cox regression model with randomly censored covariates
Atem, Folefac D.
Matsouaka, Roland A.
Zimmern, Vincent E.
[J]. BIOMETRICAL JOURNAL, 2019, 61 (04) : 1020 - 1032
[27] A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates
Zhang, Nanhua
Little, Roderick J.
[J]. BIOMETRICS, 2012, 68 (03) : 933 - 942
[28] Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates
Wang, Zhi-Qiang
Tang, Nian-Sheng
[J]. BAYESIAN ANALYSIS, 2020, 15 (02): : 579 - 604
[29] Cox regression analysis with missing covariates via nonparametric multiple imputation
Hsu, Chiu-Hsieh
Yu, Mandi
[J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2019, 28 (06) : 1676 - 1688
[30] On using the Cox proportional hazards model with missing covariates
Paik, MC
Tsai, WY
[J]. BIOMETRIKA, 1997, 84 (03) : 579 - 593

← 1 2 3 4 5 →