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Mixture Cure Model with Random Effects for Clustered Interval-censored Survival Data by Liming Xiang; Xiangmei Ma; Kelvin K. W. Yau is a Mathematics article available to read on EtoBox.

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## Abstract 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 wh

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Author

Liming Xiang; Xiangmei Ma; Kelvin K. W. Yau

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2011

Language

EN

Field

Mathematics (Physical Sciences)