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Estimation method of the semiparametric mixture cure gamma frailty model
Author(s) -
Peng Yingwei,
Zhang Jiajia
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3358
Subject(s) - unobservable , mixture model , covariate , expectation–maximization algorithm , computer science , imputation (statistics) , statistics , accelerated failure time model , econometrics , maximum likelihood , mathematics , artificial intelligence , missing data , machine learning
Mixture cure frailty model has been proposed to analyze censored survival data with a cured fraction and unobservable information among the uncured patients. Different from a usual mixture cure model, the frailty model is employed to model the latency component in the mixture cure frailty model. In this paper, we extend the mixture cure frailty model by incorporating covariates into both the cure rate and the latency distribution parts of the model and propose a semiparametric estimation method for the model. The Expectation Maximization (EM) algorithm and the multiple imputation method are employed to estimate parameters of interest. In the simulation study, we show that both estimation methods work well. To illustrate, we apply the model and the proposed methods to a data set of failure times from bone marrow transplant patients. Copyright © 2008 John Wiley & Sons, Ltd.