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Mixture cure model with random effects for clustered interval‐censored survival data
Author(s) -
Xiang Liming,
Ma Xiangmei,
Yau Kelvin K. W.
Publication year - 2011
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.4170
Subject(s) - censoring (clinical trials) , proportional hazards model , statistics , logistic regression , accelerated failure time model , survival analysis , computer science , random effects model , confidence interval , expectation–maximization algorithm , mixture model , interval (graph theory) , survival function , mathematics , maximum likelihood , medicine , meta analysis , combinatorics
The mixture cure model is an effective tool for analysis of survival data with a cure fraction. This approach integrates the logistic regression model for the proportion of cured subjects and the survival model (either the Cox proportional hazards or accelerated failure time model) for uncured subjects. Methods based on the mixture cure model have been extensively investigated in the literature for data with exact failure/censoring times. In this paper, we propose a mixture cure modeling procedure for analyzing clustered and interval‐censored survival time data by incorporating random effects in both the logistic regression and PH regression components. Under the generalized linear mixed model framework, we develop the REML estimation for the parameters, as well as an iterative algorithm for estimation of the survival function for interval‐censored data. The estimation procedure is implemented via an EM algorithm. A simulation study is conducted to evaluate the performance of the proposed method in various practical situations. To demonstrate its usefulness, we apply the proposed method to analyze the interval‐censored relapse time data from a smoking cessation study whose subjects were recruited from 51 zip code regions in the southeastern corner of Minnesota. Copyright © 2011 John Wiley & Sons, Ltd.