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Biostatistics 3:387-405 (2002)
© 2002 Oxford University Press

Maximum likelihood estimation in random effects cure rate models with nonignorable missing covariates

Amy H. Herring* and Joseph G. Ibrahim

Department of Biostatistics, The University of North Carolina at Chapel Hill, Campus Box 7420, Chapel Hill, NC 27599, USA aherring{at}bios.unc.edu
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, 655 Huntington Avenue, Boston MA 02115, USA

*To whom correspondence should be addressed

We introduce a method of parameter estimation for a random effects cure rate model. We also propose a methodology that allows us to account for nonignorable missing covariates in this class of models. The proposed method corrects for possible bias introduced by complete case analysis when missing data are not missing completely at random and is motivated by data from a pair of melanoma studies conducted by the Eastern Cooperative Oncology Group in which clustering by cohort or time of study entry was suspected. In addition, these models allow estimation of cure rates, which is desirable when we do not wish to assume that all subjects remain at risk of death or relapse from disease after sufficient follow-up. We develop an EM algorithm for the model and provide an efficient Gibbs sampling scheme for carrying out the E-step of the algorithm.

Keywords: Cure rate model; Gibbs sampling; Missing covariates; Monte Carlo EM algorithm; Nonignorable missing data; Random effects; Survival analysis


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