Bayesian semiparametric dynamic frailty models for multiple event time data.
Many biomedical studies collect data on times of occurrence for a health event that can occur repeatedly, such as infection, hospitalization, recurrence of disease, or tumor onset. To analyze such data, it is necessary to account for within-subject dependency in the multiple event times. Motivated by data from studies of palpable tumors, this article proposes a dynamic frailty model and Bayesian semiparametric approach to inference. The widely used shared frailty proportional hazards model is generalized to allow subject-specific frailties to change dynamically with age while also accommodating nonproportional hazards. Parametric assumptions on the frailty distribution are avoided by using Dirichlet process priors for a shared frailty and for multiplicative innovations on this frailty. By centering the semiparametric model on a conditionally conjugate dynamic gamma model, we facilitate posterior computation and lack-of-fit assessments of the parametric model. Our proposed method is demonstrated using data from a cancer chemoprevention study.
Pennell
Michael L
ML
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. pennell@niehs.nih.gov
Dunson
David B
DB
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D016644:Canthaxanthin / Q000494:pharmacology; D003627:Data Interpretation, Statistical; D006801:Humans; D008325:Mammary Neoplasms, Experimental / Q000517:prevention & control; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D051381:Rats; D013997:Time Factors
D016644:Canthaxanthin