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.

journal_name

Biometrics

journal_title

Biometrics

authors

Chen Y,Hanson T,Zhang J

doi

10.1111/biom.12104

subject

Has Abstract

pub_date

2014-03-01 00:00:00

pages

192-201

issue

1

eissn

0006-341X

issn

1541-0420

journal_volume

70

pub_type

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