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Lifetime Data Analysis

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03-03-2022

Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models

Authors: Wenjing Yin, Sihai Dave Zhao, Feng Liang

Published in: Lifetime Data Analysis | Issue 2/2022

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Abstract

For high dimensional gene expression data, one important goal is to identify a small number of genes that are associated with progression of the disease or survival of the patients. In this paper, we consider the problem of variable selection for multivariate survival data. We propose an estimation procedure for high dimensional accelerated failure time (AFT) models with bivariate censored data. The method extends the Buckley-James method by minimizing a penalized \(L_2\) loss function with a penalty function induced from a bivariate spike-and-slab prior specification. In the proposed algorithm, censored observations are imputed using the Kaplan-Meier estimator, which avoids a parametric assumption on the error terms. Our empirical studies demonstrate that the proposed method provides better performance compared to the alternative procedures designed for univariate survival data regardless of whether the true events are correlated or not, and conceptualizes a formal way of handling bivariate survival data for AFT models. Findings from the analysis of a myeloma clinical trial using the proposed method are also presented.

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Title: Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models
Authors: Wenjing Yin
Sihai Dave Zhao
Feng Liang
Publication date: 03-03-2022
Publisher: Springer US
Published in: Lifetime Data Analysis / Issue 2/2022
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI: https://doi.org/10.1007/s10985-022-09549-5

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