_:n299b451a1b6c48cf87e27db12d75df1fxb36
<http://schema.org/description>
"Cure models in survival analysis deal with populations in which a part of the\nindividuals cannot experience the event of interest. Mixture cure models\nconsider the target population as a mixture of susceptible and non-susceptible\nindividuals. The statistical analysis of these models focuses on examining the\nprobability of cure (incidence model) and inferring on the time-to-event in the\nsusceptible subpopulation (latency model).\n  Bayesian inference on mixture cure models has typically relied upon Markov\nchain Monte Carlo (MCMC) methods. The integrated nested Laplace approximation\n(INLA) is a recent and attractive approach for doing Bayesian inference. INLA\nin its natural definition cannot fit mixture models but recent research has new\nproposals that combine INLA and MCMC methods to extend its applicability to\nthem (Bivand et al., 2014, Gómez-Rubio et al., 2017, Gómez-Rubio and Rue,\n2018.\n  This paper focuses on the implementation of INLA in mixture cure models. A\ngeneral mixture cure survival model with covariate information for the latency\nand the incidence model within a general scenario with censored and\nnon-censored information is discussed. The fact that non-censored individuals\nundoubtedly belong to the uncured population is a valuable information that was\nincorporated in the inferential process."
<https://api.deepai.org/profile/virgilio-gomez-rubio>