title: Bayesian path specific frailty models for multi-state survival data with applications.
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.
authors.affiliation: Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, São Carlos, SP, Brazil.
authors.forename: Mário
authors.initials: M
authors.lastname: de Castro
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.affiliation: Novartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts, U.S.A.
authors.forename: Yuanye
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D016026:Bone Marrow Transplantation / Q000401:mortality*; D003627:Data Interpretation, Statistical; D006801:Humans; D007938:Leukemia / Q000401:mortality* / Q000628:therapy*; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D016896:Treatment Outcome
keywords: Gamma frailty; Gap time; Gibbs sampler; Piecewise exponential model; Survival analysis