Bayesian data integration and variable selection for pan‐cancer survival prediction using protein expression data
Arnab Kumar Maity,
Anirban Bhattacharya,
Bani K. Mallick and
Veerabhadran Baladandayuthapani
Biometrics, 2020, vol. 76, issue 1, 316-325
Abstract:
Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1111/biom.13132
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:biomet:v:76:y:2020:i:1:p:316-325
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X
Access Statistics for this article
More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().