Bayesian Semiparametric Cure Rate Model with an Unknown Threshold

Authors


Luis E. Nieto-Barajas, Department of Statistics, ITAM, Rio Hondo 1, Tizapan San Angel, 01000, Mexico D.F., Mexico.
E-mail: lnieto@itam.mx

Abstract

Abstract.  We propose a Bayesian semiparametric model for survival data with a cure fraction. We explicitly consider a finite cure time in the model, which allows us to separate the cured and the uncured populations. We take a mixture prior of a Markov gamma process and a point mass at zero to model the baseline hazard rate function of the entire population. We focus on estimating the cure threshold after which subjects are considered cured. We can incorporate covariates through a structure similar to the proportional hazards model and allow the cure threshold also to depend on the covariates. For illustration, we undertake simulation studies and a full Bayesian analysis of a bone marrow transplant data set.

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