Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data

Authors: Pennell, Michael L.; Dunson, David B.

Source: Biometrics, Volume 62, Number 4, 1 December 2006 , pp. 1044-1052(9)

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Abstract:

<sc>Summary</sc> 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.

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1541-0420.2006.00571.x

Affiliations: 1: Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, U.S.A.

Publication date: 2006-12-01