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Generalized method of moments estimation for linear regression with clustered failure time data

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  • Hui Li
  • Guosheng Yin

Abstract

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. Copyright 2009, Oxford University Press.

Suggested Citation

  • Hui Li & Guosheng Yin, 2009. "Generalized method of moments estimation for linear regression with clustered failure time data," Biometrika, Biometrika Trust, vol. 96(2), pages 293-306.
  • Handle: RePEc:oup:biomet:v:96:y:2009:i:2:p:293-306
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    File URL: http://hdl.handle.net/10.1093/biomet/asp005
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    Cited by:

    1. Hui Li & Xiaogang Duan & Guosheng Yin, 2016. "Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1124-1139, December.
    2. Shang, Wenpeng & Wang, Xiao, 2017. "The generalized moment estimation of the additive–multiplicative hazard model with auxiliary survival information," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 154-169.
    3. Wenjing Yin & Sihai Dave Zhao & Feng Liang, 2022. "Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 282-318, April.
    4. Yifei Sun & Kwun Chuen Gary Chan & Jing Qin, 2018. "Simple and fast overidentified rank estimation for right†censored length†biased data and backward recurrence time," Biometrics, The International Biometric Society, vol. 74(1), pages 77-85, March.

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