期刊
BIOMETRICS
卷 62, 期 4, 页码 1044-1052出版社
WILEY
DOI: 10.1111/j.1541-0420.2006.00571.x
关键词
breast cancer; chemoprevention; Dirichlet process; nonparametric Bayes; palpable tumors; survival analysis; tumor multiplicity data
资金
- Intramural NIH HHS Funding Source: Medline
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.
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