Existing cure-rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. This paper proposes a novel class of cure-rate model, the transform-both-sides cure-rate model (TBSCRM), that can be used to make inferences about both the cure-rate and the survival quantiles. We develop the Bayesian inference about the covariate effects on the cure-rate as well as on the survival quantiles via Markov Chain Monte Carlo (MCMC) tools. We also show that the TBSCRM-based Bayesian method outperforms existing cure-rate models based methods in our simulation studies and in application to the breast cancer survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database.
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| http://dx.doi.org/10.1002/bimj.201500111 | DOI Listing |
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