Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#258069] of David B. Dunson


Papers Published

  1. Pennell, ML; Dunson, DB, Bayesian semiparametric dynamic frailty models for multiple event time data., Biometrics, vol. 62 no. 4 (December, 2006), pp. 1044-1052, ISSN 0006-341X [html], [doi]
    (last updated on 2019/05/24)

    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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320