This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 624
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #307186
Title: Mixture Cure Model with Random Effects for Interval-Censored Survival Data
Author(s): Liming Xiang*+ and Xiangmei Ma
Companies: Nanyang Technological University and Nanyang Technological University
Address: Division of Mathematical Sciences, Singapore, 637371, Singapore
Keywords: Cure fraction ; EM algorithm ; Interval censoring ; Mixture model ; Random effects

The mixture cure model provides an effective way of analyzing survival data with a cure fraction. This approach integrates both logistic regression for the proportion of cured subjects and survival model for those uncured subjects, and has 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 data by allowing random effects in both the logistic and the proportional hazards regression components. Under the GLMM framework, we develop the REML estimation for parameters, embedding with Turnbull's algorithm for the estimation of survival function for interval-censored data. The estimation procedure is implemented via an EM algorithm. The method is applied to data from a smoking cessation study and its performance is evaluated through simulations.

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