title: Accelerated hazards model based on parametric families generalized with Bernstein polynomials.
abstract: 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.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina, U.S.A.
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Chen
authors.forename: Timothy
authors.initials: T
authors.lastname: Hanson
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000188:drug therapy; D002330:Carmustine / Q000627:therapeutic use; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005910:Glioma / Q000188:drug therapy; D006801:Humans; D011108:Polymers / Q000494:pharmacology; D016016:Proportional Hazards Models; D016019:Survival Analysis
chemical_list: D011108:Polymers; D002330:Carmustine
keywords: Accelerated hazards model; Bayesian nonparametric prior; Survival analysis; Time dependent covariate