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用于复发事件数据的加速强度脆弱性模型。

Accelerated intensity frailty model for recurrent events data.

作者信息

Liu Bo, Lu Wenbin, Zhang Jiajia

机构信息

Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.

Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, U.S.A.

出版信息

Biometrics. 2014 Sep;70(3):579-87. doi: 10.1111/biom.12163. Epub 2014 Mar 3.

Abstract

In this article we propose an accelerated intensity frailty (AIF) model for recurrent events data and derive a test for the variance of frailty. In addition, we develop a kernel-smoothing-based EM algorithm for estimating regression coefficients and the baseline intensity function. The variance of the resulting estimator for regression parameters is obtained by a numerical differentiation method. Simulation studies are conducted to evaluate the finite sample performance of the proposed estimator under practical settings and demonstrate the efficiency gain over the Gehan rank estimator based on the AFT model for counting process (Lin et al., 1998). Our method is further illustrated with an application to a bladder tumor recurrence data.

摘要

在本文中,我们针对复发事件数据提出了一种加速强度脆弱性(AIF)模型,并推导了一种用于检验脆弱性方差的方法。此外,我们开发了一种基于核平滑的期望最大化(EM)算法,用于估计回归系数和基线强度函数。回归参数估计量的方差通过数值微分方法获得。进行了模拟研究,以评估所提出的估计量在实际情况下的有限样本性能,并证明相对于基于计数过程的加速失效时间(AFT)模型的Gehan秩估计量(Lin等人,1998年)在效率上的提升。我们的方法通过应用于膀胱肿瘤复发数据得到了进一步说明。

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