Random effects in promotion time cure rate models
Tipo
Artigo
Data de publicação
2012
Periódico
Computational Statistics and Data Analysis
Citações (Scopus)
15
Autores
Carvalho Lopes C.M.
Bolfarine H.
Bolfarine H.
Orientador
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Título de Volume
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Resumo
In this paper, a survival model with long-term survivors and random effects, based on the promotion time cure rate model formulation for models with a surviving fraction is investigated. We present Bayesian and classical estimation approaches. The Bayesian approach is implemented using a Markov chain Monte Carlo (MCMC) based on the Metropolis-Hastings algorithms. For the second one, we use restricted maximum likelihood (REML) estimators. A simulation study is performed to evaluate the accuracy of the applied techniques for the estimates and their standard deviations. An example on an oropharynx cancer study is used to illustrate the model and the estimation approaches considered in the study. © 2011 Elsevier B.V. All rights reserved.
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Assuntos Scopus
Bayesian approaches , Cure rate , Long-term survivors , Markov chain monte carlo , Metropolis-Hastings , Metropolis-Hastings algorithm , Oropharynx cancer , Random effects , REML , Restricted maximum likelihood , Simulation studies , Standard deviation , Survival model , Surviving fractions