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Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes

Authors

  • Xiuyu J. Cong,

    Corresponding author
    1. Department of Biometrics and Data Management, Boehringer Ingelheim Pharmaceuticals, P.O. Box 368, Ridgefield, Connecticut 06877, U.S.A.
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  • Guosheng Yin,

    Corresponding author
    1. Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.
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  • Yu Shen

    Corresponding author
    1. Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.
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email:xcong@rdg.boehringer-ingelheim.com

email:gsyin@mdanderson.org

email:yshen@mdanderson.org

Abstract

Summary We consider modeling correlated survival data when cluster sizes may be informative to the outcome of interest based on a within-cluster resampling (WCR) approach and a weighted score function (WSF) method. We derive the large sample properties for the WCR estimators under the Cox proportional hazards model. We establish consistency and asymptotic normality of the regression coefficient estimators, and the weak convergence property of the estimated baseline cumulative hazard function. The WSF method is to incorporate the inverse of cluster sizes as weights in the score function. We conduct simulation studies to assess and compare the finite-sample behaviors of the estimators and apply the proposed methods to a dental study as an illustration.

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