Open AccessExponentiated Weibull Models Applied to Medical Data in Presence of Right-censoring, Cure Fraction and Covariates
Author(s) -
Edson Zangiacomí Martínez,
Bruno Caparroz Lopes de Freitas,
Jorge Alberto Achcar,
Davi Casale Aragon,
Marcos Vinicius de Oliveira Peres
Publication year - 2021
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-1266
Subject(s) - weibull distribution , markov chain monte carlo , censoring (clinical trials) , statistics , covariate , bayesian probability , mathematics , frequentist inference , fraction (chemistry) , estimator , accelerated failure time model , econometrics , bayesian inference , chemistry , organic chemistry
Cure fraction models have been widely used to analyze survival data in which a proportion of the individuals isnot susceptible to the event of interest. This article considers frequentist and Bayesian methods to estimate the unknown model parameters of the exponentiated Weibull (EW) distribution considering right-censored survival data with a cure fraction and covariates. The EW distribution is as an extension to the Weibull distribution by considering an additional shape parameter to the model. We consider four types of cure fraction models: the mixture cure fraction (MCF), the nonmixture cure fraction (NMCF), the complementary promotion time cure (CPTC), and the cure rate proportional odds (CRPO) models. Bayesian inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods. A simulation study was conducted to examine the performance of the maximum likelihood estimators for different sample sizes. Two real datasets were considered to illustrate the applicability of the proposed model. The EW distribution and its sub-models have the flexibility to accommodate different shapes for the hazard function and should be an attractive choice for survival data analysis when a cure fraction is present.