Semiparametric transformation models for the case-cohort study
Semiparametric transformation models for the case-cohort study
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DOI：
10.1093/biomet/93.1.207
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发表时间：
2006-03-01
期刊：
影响因子：
2.7
通讯作者：
Tsiatis, AA
中科院分区：
文献类型：
--
作者：
Lu, WB;Tsiatis, AA
研究了一类用于分析病例 - 队列设计生存数据的半参数变换模型，该设计由普伦蒂斯（Prentice，1986）提出。提出了加权估计方程，用于同时估计回归参数和变换函数。结果表明，所得回归估计量渐近正态，其方差 - 协方差矩阵具有封闭形式，并且可以通过通常的代入法进行一致估计。模拟研究表明，所提出的方法适用于实际应用。还给出了对社区动脉粥样硬化风险研究中的一个病例 - 队列数据集的应用，以说明该方法。
A general class of semiparametric transformation models is studied for analysing survival data from the case-cohort design, which was introduced by Prentice (1986). Weighted estimating equations are proposed for simultaneous estimation of the regression parameters and the transformation function. It is shown that the resulting regression estimators are asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual plug-in method. Simulation studies show that the proposed approach is appropriate for practical use. An application to a case-cohort dataset from the Atherosclerosis Risk in Communities study is also given to illustrate the methodology.