Semiparametric transformation models with random effects for recurrent events
Semiparametric transformation models with random effects for recurrent events
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DOI：
10.1198/016214506000001239
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发表时间：
2007-03-01
影响因子：
3.7
通讯作者：
Lin, D. Y.
中科院分区：
文献类型：
--
作者：
Zeng, Donglin;Lin, D. Y.
在本文中，我们研究了一类对计数过程的强度函数具有随机效应的半参数变换模型。这些模型在制定可能与时间相关的协变量对重复事件发展的影响方面提供了相当大的灵活性，同时考虑了同一受试者内重复事件时间的依赖性。我们证明这些模型参数的非参数最大似然估计（NPMLE）是一致的且渐近正态的。回归参数估计量的极限协方差矩阵达到半参数效率界限并且可以一致地估计。累积强度函数的任何平滑函数的估计器的极限协方差函数也可以被一致地估计。我们开发了一种简单且稳定的 EM 算法来计算 NPMLE 以及方差和协方差估计量。仿真研究表明所提出的方法在实际情况中表现良好。提供了两项医学研究作为说明。
In this article we study a class of semiparametric transformation models with random effects for the intensity function of the counting process. These models provide considerable flexibility in formulating the effects of possibly time-dependent covariates on the developments of recurrent events while accounting for the dependence of the recurrent event times within the same subject. We show that the nonparametric maximum likelihood estimators (NPMLEs) for the parameters of these models are consistent and asymptotically normal. The limiting covariance matrices for the estimators of the regression parameters achieve the semiparametric efficiency bounds and can be consistently estimated. The limiting covariance function for the estimator of any smooth functional of the cumulative intensity function also can be consistently estimated. We develop a simple and stable EM algorithm to compute the NPMLEs as well as the variance and covariance estimators. Simulation studies demonstrate that the proposed methods perform well in practical situations. Two medical studies are provided for illustrations.