Accelerated hazards model based on parametric families generalized with Bernstein polynomials. Issue 1 (21st November 2013) - Chen, Yuhui; Hanson, Timothy; Zhang, Jiajia
Summary 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.