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Fully semiparametric Bayesian approach for modeling survival data with cure fraction
Author(s) -
Demarqui Fabio N.,
Dey Dipak K.,
Loschi Rosangela H.,
Colosimo Enrico A.
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201200205
Subject(s) - bayesian probability , approximate bayesian computation , computer science , grid , gibbs sampling , randomness , bayesian inference , hierarchical database model , random effects model , mathematics , algorithm , statistics , artificial intelligence , data mining , inference , medicine , meta analysis , geometry
In this paper, we consider a piecewise exponential model (PEM) with random time grid to develop a full semiparametric Bayesian cure rate model. An elegant mechanism enjoying several attractive features for modeling the randomness of the time grid of the PEM is assumed. To model the prior behavior of the failure rates of the PEM we assume a hierarchical modeling approach that allows us to control the degree of parametricity in the right tail of the survival curve. Properties of the proposed model are discussed in detail. In particular, we investigate the impact of assuming a random time grid for the PEM on the estimation of the cure fraction. We further develop an efficient collapsed Gibbs sampler algorithm for carrying out posterior computation. A Bayesian diagnostic method for assessing goodness of fit and performing model comparisons is briefly discussed. Finally, we illustrate the usefulness of the new methodology with the analysis of a melanoma clinical trial that has been discussed in the literature.