Estimation of shared Gamma frailty models by a modified EM algorithm
Estimation of shared Gamma frailty models by a modified EM algorithm
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DOI：
10.1016/j.csda.2004.08.010
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发表时间：
2006-01-30
影响因子：
1.8
通讯作者：
Yu, BB
中科院分区：
文献类型：
--
作者：
Yu, BB
标准生存模型假定生存时间和脆弱模型之间的独立性，当生存数据相关时，通过引入随机效应（脆弱）为标准生存模型提供了有用的扩展。为了寻找共享脆弱性模型的参数，提出了几种估计方法。其中，EM算法（生存分析-删减和截断数据的技术，1997）和惩罚似然法（惩罚生存模型和脆弱性，技术报告第66号，梅奥基金会，2000）是两个比较流行的方法。但是，由于方差估计涉及到矩阵逆的计算，目前的方法无法处理大量聚类的数据。本文提出了一种改进的EM算法来求解共享漏洞模型。该方法利用标准的统计程序来寻找最大似然估计（MLE），可以处理具有大量聚类和不同事件时间的数据集。参数的置信区间可以通过多次插值来构造。模拟研究进行了比较不同的方法的脆弱性模型。(c) 2004 Elsevier B.V.版权所有
Standard survival models assume independence between survival times and frailty models provide a useful extension of the standard survival models by introducing a random effect (frailty) when the survival data are correlated. Several estimation methods have been proposed to find the parameters of shared frailty models. Among them, the EM algorithm (Survival Analysis-Techniques for Censored and Truncated Data, 1997) and the penalized likelihood method (Penalized Survival Models and Frailty, Technical Report No. 66, Mayo Foundation, 2000) are two popular ones. However, the variance estimates involve the calculation of matrix inverse, so the current methods are not able to handle the data with a large number of clusters. This paper provides a modified EM algorithm for the shared frailty models. The new method utilizes standard statistical procedures to find the maximum likelihood estimates (MLE) and it can handle data sets with large numbers of clusters and distinct event times. The confidence intervals of the parameters can be constructed by multiple imputation. Simulation studies were carried out to compare different approaches for the frailty models. (c) 2004 Elsevier B.V. All rights reserved.