Mixture Cure Rate Modelling for Cancer Survival Data A Comparative Study (original) (raw)

2018

Abstract

Introduction: Heterogeneity is a common phenomenon in cancer survival. Cancer survival probability varies hugely with respect to the prognostic factors. When the patients have long- term survival and the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals, who will never experience it and are heterogeneous, mixture cure rate model is the alternate method.Methods: For illustration, breast cancer patients registered during 2006 followed-up till 2014was considered, and further followed-up till 2016 was considered for validation. Kaplan-Meier (K-M) method, mixture distribution (MD) models and mixture cure rate (MCR) models were used for estimating the probabilities. Anderson-Darling Statistics, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Kullback-Leibler survival (KLS) divergence were used for model identification.Results: The mixing proportion was estimated as 0.52 for the...

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