Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 62 (4), 1044-52

Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data

Affiliations

Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data

Michael L Pennell et al. Biometrics.

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.

Similar articles

See all similar articles

Cited by 6 PubMed Central articles

  • Estimating Effectiveness in HIV Prevention Trials With a Bayesian Hierarchical Compound Poisson Frailty Model
    RY Coley et al. Stat Med 35 (15), 2609-34. PMID 26869051.
    Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention …
  • The Local Dirichlet Process
    Y Chung et al. Ann Inst Stat Math 63 (1), 59-80. PMID 23645935.
    As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose a local Dirichlet process (lDP). The lDP provides a prior distribution for a c …
  • Bayesian Local Influence for Survival Models
    JG Ibrahim et al. Lifetime Data Anal 17 (1), 43-70. PMID 20526807. - Review
    The aim of this paper is to develop a Bayesian local influence method (Zhu et al. 2009, submitted) for assessing minor perturbations to the prior, the sampling distributi …
  • Bayesian Nonparametric Functional Data Analysis Through Density Estimation
    A Rodríguez et al. Biometrika 96 (1), 149-162. PMID 19262739.
    In many modern experimental settings, observations are obtained in the form of functions, and interest focuses on inferences on a collection of such functions. We propose …
  • Kernel Stick-Breaking Processes
    DB Dunson et al. Biometrika 95 (2), 307-323. PMID 18800173.
    We propose a class of kernel stick-breaking processes for uncountable collections of dependent random probability measures. The process is constructed by first introducin …
See all "Cited by" articles

Publication types

LinkOut - more resources

Feedback