Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach.

ABSTRACT: We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses, and not just only one of them. In addition, we develop a survival prediction model based on data integration. This article is motivated by the Cancer Genomic Atlas (TCGA) databank, which is currently the largest genomics and transcriptomics database. The data contain cancer survival information along with cancer stages for each patient. Furthermore, it contains Reverse-phase Protein Array (RPPA) measurements for each individual, which are the predictors associated with these responses. The biological motivation is to identify the major actionable protei

SUBMITTER: Maity AK 

PROVIDER: S-EPMC7729996 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature