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
Biometricsjournal_title
Biometricsauthors
Chen Y,Hanson T,Zhang Jdoi
10.1111/biom.12104subject
Has Abstractpub_date
2014-03-01 00:00:00pages
192-201issue
1eissn
0006-341Xissn
1541-0420journal_volume
70pub_type
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