Bayesian path specific frailty models for multi-state survival data with applications.

TitleBayesian path specific frailty models for multi-state survival data with applications.
Publication TypeJournal Article
Year of Publication2015
AuthorsDE Castro, Mário, Ming-Hui Chen, and Yuanye Zhang
JournalBiometrics
Volume71
Issue3
Pagination760-71
Date Published2015 Sep
ISSN1541-0420
KeywordsBayes Theorem, Bone Marrow Transplantation, Data Interpretation, Statistical, Humans, Leukemia, Models, Statistical, Outcome Assessment, Health Care, Prevalence, Reproducibility of Results, Risk Assessment, Sensitivity and Specificity, Survival Analysis, Treatment Outcome
Abstract

Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.

DOI10.1111/biom.12298
Alternate JournalBiometrics
Original PublicationBayesian path specific frailty models for multi-state survival data with applications.
PubMed ID25762198
PubMed Central IDPMC4567543
Grant ListCA 74015 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
GM 70335 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA074015 / CA / NCI NIH HHS / United States
Project: