期刊
BIOMETRICS
卷 70, 期 1, 页码 192-201出版社
WILEY
DOI: 10.1111/biom.12104
关键词
Accelerated hazards model; Bayesian nonparametric prior; Survival analysis; Time dependent covariate
资金
- NCI [R03CA165110]
- University of South Carolina
A transformed Bernstein polynomial that is centered at standard parametric families, such as Weibull or log-logistic, is proposed for use in the accelerated hazards model. This class provides a convenient way towards creating a Bayesian nonparametric prior for smooth densities, blending the merits of parametric and nonparametric methods, that is amenable to standard estimation approaches. For example optimization methods in SAS or R can yield the posterior mode and asymptotic covariance matrix. This novel nonparametric prior is employed in the accelerated hazards model, which is further generalized to time-dependent covariates. The proposed approach fares considerably better than previous approaches in simulations; data on the effectiveness of biodegradable carmustine polymers on recurrent brain malignant gliomas is investigated.
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