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Bayesian Semiparametric Models for Survival Data with a Cure Fraction
Author(s) -
Ibrahim Joseph G.,
Chen MingHui,
Sinha Debajyoti
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00383.x
Subject(s) - prior probability , bayesian probability , inference , smoothing , computer science , class (philosophy) , bayesian inference , posterior probability , semiparametric regression , fraction (chemistry) , econometrics , mathematics , artificial intelligence , statistics , nonparametric statistics , chemistry , organic chemistry
Summary. We propose methods for Bayesian inference for a new class of semiparametric survival models with a cure fraction. Specifically, we propose a semiparametric cure rate model with a smoothing parameter that controls the degree of parametricity in the right tail of the survival distribution. We show that such a parameter is crucial for these kinds of models and can have an impact on the posterior estimates. Several novel properties of the proposed model are derived. In addition, we propose a class of improper noninformative priors based on this model and examine the properties of the implied posterior. Also, a class of informative priors based on historical data is proposed and its theoretical properties are investigated. A case study involving a melanoma clinical trial is discussed in detail to demonstrate the proposed methodology.