Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction

Cherry Gupta, Juliana Cobre, Adriano Polpo, Debajyoti Sinha

Research output: Contribution to journalArticle

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)1164-1177
Number of pages14
JournalBiometrical Journal
Issue number5
Publication statusPublished - Sep 2016
Externally publishedYes

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