Biometrika Advance Access originally published online on April 21, 2009
Biometrika 2009 96(2):293-306; doi:10.1093/biomet/asp005
Article |
Generalized method of moments estimation for linear regression with clustered failure time data
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China li_hui{at}bnu.edu.cn
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A. gsyin{at}mdanderson.org
Received for publication 1 November 2006. Revision received 1 September 2008.
We propose a generalized method of moments approach to the accelerated failure time model with correlated survival data. We study the semiparametric rank estimator using martingale-based moments. We circumvent direct estimation of correlation parameters by concatenating the moments and minimizing a quadratic objective function. We establish the consistency and asymptotic normality of the parameter estimators, and derive the limiting distribution of the objective function. We carry out simulation studies to examine the finite-sample properties of the method, and demonstrate its substantial efficiency gain over the conventional method. Finally, we illustrate the new proposal with an example from a diabetic retinopathy study.
Key Words: Accelerated failure time model • Asymptotic normality • Correlated survival data • Estimation efficiency • Moment condition • Rank estimation • Semiparametric model
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