Bayesian semiparametric dynamic frailty models for multiple event time data

Biometrics. 2006 Dec;62(4):1044-52. doi: 10.1111/j.1541-0420.2006.00571.x.

Abstract

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.

Publication types

  • Research Support, N.I.H., Intramural

MeSH terms

  • Algorithms
  • Animals
  • Bayes Theorem*
  • Biometry
  • Canthaxanthin / pharmacology
  • Data Interpretation, Statistical
  • Humans
  • Mammary Neoplasms, Experimental / prevention & control
  • Markov Chains
  • Models, Statistical*
  • Monte Carlo Method
  • Rats
  • Time Factors

Substances

  • Canthaxanthin