High-dimensional variable selection in meta-analysis for censored data.

ABSTRACT: This article considers the problem of selecting predictors of time to an event from a high-dimensional set of candidate predictors using data from multiple studies. As an alternative to the current multistage testing approaches, we propose to model the study-to-study heterogeneity explicitly using a hierarchical model to borrow strength. Our method incorporates censored data through an accelerated failure time model. Using a carefully formulated prior specification, we develop a fast approach to predictor selection and shrinkage estimation for high-dimensional predictors. For model fitting, we develop a Monte Carlo expectation maximization (MC-EM) algorithm to accommodate censored data. The proposed approach, which is related to the relevance vector machine (RVM), relies on maximum a poste

SUBMITTER: Liu F 

PROVIDER: S-EPMC3478096 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature