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Author (up) de la Cruz, R.; Fuentes, C.; Padilla, O. doi  openurl
  Title A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism Type
  Year 2023 Publication Entropy Abbreviated Journal Entropy  
  Volume 25 Issue 1 Pages 56  
  Keywords Bayesian inference; MCMC methods; mixture cure rate models; recidivism; Weibull distribution  
  Abstract Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1099-4300 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000914983600001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1720  
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