Bayesian variable selection for parametric survival model with applications to cancer omics data.

ABSTRACT:

Background

Modeling thousands of markers simultaneously has been of great interest in testing association between genetic biomarkers and disease or disease-related quantitative traits. Recently, an expectation-maximization (EM) approach to Bayesian variable selection (EMVS) facilitating the Bayesian computation was developed for continuous or binary outcome using a fast EM algorithm. However, it is not suitable to the analyses of time-to-event outcome in many public databases such as The Cancer Genome Atlas (TCGA).

Results

We extended the EMVS to high-dimensional parametric survival regression framework (SurvEMVS). A variant of cyclic coordinate descent (CCD) algorithm was used for efficient iteration in M-step, and the extended Bayesian information criteria (EBIC) was emplo

SUBMITTER: Duan W 

PROVIDER: S-EPMC6218990 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature