A Bayesian multi-risks survival (MRS) model in the presence of double censorings.

TitleA Bayesian multi-risks survival (MRS) model in the presence of double censorings.
Publication TypeJournal Article
Year of Publication2020
AuthorsDE Castro, Mário, Ming-Hui Chen, Yuanye Zhang, and Anthony V. D'Amico
JournalBiometrics
Volume76
Issue4
Pagination1297-1309
Date Published2020 12
ISSN1541-0420
KeywordsAlgorithms, Bayes Theorem, Humans, Incidence, Male, Markov Chains, Survival Analysis
Abstract

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.

DOI10.1111/biom.13228
Alternate JournalBiometrics
Original PublicationA Bayesian multi-risks survival (MRS) model in the presence of double censorings.
PubMed ID31994171
PubMed Central IDPMC7384972
Grant List306171/2017-1 / / Conselho Nacional de Desenvolvimento Científico e Tecnológico /
R01 GM070335 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: