淡江大學機構典藏:Item 987654321/97758
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/97758


    Title: A frailty model approach for regression analysis of bivariate interval-censored survival data.
    Authors: Wen, Chi-Chung;Chen, Yi-Hau
    Contributors: 淡江大學數學學系
    Keywords: Correlated data;Interval censoring;Proportional hazards;Self-consistency
    Date: 2013-01-01
    Issue Date: 2014-04-21
    Publisher: Taipei: Academia Sinica * Institute of Statistical Science
    Abstract: Owing to the fact that general semiparametric inference procedures are
    still underdeveloped for multivariate interval-censored event time data, we propose
    semiparametric maximum likelihood estimation for the gamma-frailty Cox
    model under mixed-case interval censoring. We establish the consistency of the
    semiparametric maximum likelihood estimator (SPMLE) for the model parameters,
    including the regression coefficients and the cumulative hazard functions in
    the Cox model, and the variance of the gamma frailty. The SPMLEs of the cumulative
    hazard functions are shown to have a n
    1/3
    -rate of convergence, while those of
    the regression coefficients and the frailty variance have a n
    1/2
    -rate of convergence;
    here n denotes the number of study units. The asymptotic normality of the regression
    coefficients and the frailty variance is also established, with the asymptotic
    variance given by the inverse of the efficient Fisher information matrix. A profilelikelihood
    approach is proposed for estimating the asymptotic variance. Based on
    the self-consistency equations and the contraction principle, we propose a stable
    and efficient computation algorithm. Simulation results reveal that the large sample
    theories work quite well in finite samples. We analyze a dataset from an AIDS
    clinical trial by the proposed methods to assess the effects of the baseline CD4 cell
    counts on the times to CMV shedding in blood and urine.
    Relation: Statistica Sinica 23(1), pp.383-408
    DOI: 10.5705/ss.2011.151
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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