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Mixture cure model with random effects for the analysis of a multi-center tonsil cancer study

Authors

  • Yingwei Peng,

    Corresponding author
    1. Department of Community Health and Epidemiology, Queen's University, Kingston, ON, Canada K7L 3N6
    2. Department of Mathematics and Statistics, Queen's University, Kingston, ON, Canada K7L 3N6
    3. Cancer Care and Epidemiology, Queen's Cancer Research Institute, Kingston, ON, Canada K7L 3N6
    • Department of Community Health and Epidemiology, Queen's University, Kingston, ON, Canada K7L 3N6
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  • Jeremy M. G. Taylor

    1. Department of Biostatistics, University of Michigan, 1420 Washington, Heights, Ann Arbor, MI 48109-2029, U.S.A.
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Abstract

Cure models for clustered survival data have the potential for broad applicability. In this paper, we consider the mixture cure model with random effects and propose several estimation methods based on Gaussian quadrature, rejection sampling, and importance sampling to obtain the maximum likelihood estimates of the model for clustered survival data with a cure fraction. The methods are flexible to accommodate various correlation structures. A simulation study demonstrates that the maximum likelihood estimates of parameters in the model tend to have smaller biases and variances than the estimates obtained from the existing methods. We apply the model to a study of tonsil cancer patients clustered by treatment centers to investigate the effect of covariates on the cure rate and on the failure time distribution of the uncured patients. The maximum likelihood estimates of the parameters demonstrate strong correlation among the failure times of the uncured patients and weak correlation among cure statuses in the same center. Copyright © 2010 John Wiley & Sons, Ltd.

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