|Title||A Bayesian multi-risks survival (MRS) model in the presence of double censorings.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||DE Castro, Mário, Ming-Hui Chen, Yuanye Zhang, and Anthony V. D'Amico|
|Date Published||2020 Dec|
|Keywords||Algorithms, Bayes Theorem, Humans, Incidence, Male, Markov Chains, Survival Analysis|
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.
|Original Publication||A Bayesian multi-risks survival (MRS) model in the presence of double censorings.|
|PubMed Central ID||PMC7384972|
|Grant List||R01 GM070335 / GM / NIGMS NIH HHS / United States |
P01 CA142538 / CA / NCI NIH HHS / United States
A Bayesian multi-risks survival (MRS) model in the presence of double censorings.