Integration of survival and binary data for variable selection and prediction: a Bayesian approach

Arnab Kumar Maity, Raymond J. Carroll and Bani K. Mallick

Journal of the Royal Statistical Society Series C, 2019, vol. 68, issue 5, 1577-1595

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 one of them. In addition, we develop a survival prediction model based on data integration. The paper is motivated by the Cancer Genomic Atlas 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 measurements for each individual, which are the predictors associated with these responses. The biological motivation is to identify the major actionable proteins associated with both survival outcomes and cancer stages. We develop a Bayesian hierarchical model to model jointly the survival time and the classification of the cancer stages. Moreover, to deal with the high dimensionality of the reverse phase protein array measurements, we use a shrinkage prior to identify significant proteins. Simulations and Cancer Genomic Atlas data analysis show that the joint integrated modelling approach improves survival prediction.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12377

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:68:y:2019:i:5:p:1577-1595

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().