用于多变量生存时间的伽马脆弱性转换模型。
Gamma frailty transformation models for multivariate survival times.
作者信息
Zeng Donglin, Chen Qingxia, Ibrahim Joseph G
机构信息
Department of Biostatistics, University of North Carolina, 3105-D McGavran-Greenberg Hall, Campus Box 7420, Chapel Hill, North Carolina, 27516, U.S.A.,
Department of Biostatistics, Vanderbilt University, 1161 21st Avenue South, S-2323 Medical Center North, Nashville, Tennessee, 37232, U.S.A.,
出版信息
Biometrika. 2009 Jun;96(2):277-291. doi: 10.1093/biomet/asp008.
We propose a class of transformation models for multivariate failure times. The class of transformation models generalize the usual gamma frailty model and yields a marginally linear transformation model for each failure time. Nonparametric maximum likelihood estimation is used for inference. The maximum likelihood estimators for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies show that the proposed estimation procedure provides asymptotically efficient estimates and yields good inferential properties for small sample sizes. The method is illustrated using data from a cardiovascular study.
我们提出了一类用于多变量失效时间的变换模型。这类变换模型推广了常见的伽马脆弱模型，并为每个失效时间产生了一个边际线性变换模型。使用非参数最大似然估计进行推断。结果表明，回归系数的最大似然估计量是一致的且渐近正态，并且它们的渐近方差达到半参数效率界。模拟研究表明，所提出的估计程序提供了渐近有效的估计，并在小样本量时具有良好的推断性质。使用一项心血管研究的数据对该方法进行了说明。
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