Ascertainment correction in frailty models for recurrent events data
Ascertainment correction in frailty models for recurrent events data
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DOI：
10.1002/sim.6968
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发表时间：
2016-10-15
影响因子：
2
通讯作者：
Putter, Hein
中科院分区：
文献类型：
--
作者：
Balan, Theodor A.;Jonker, Marianne A.;Putter, Hein
在涉及复发性事件的回顾性研究中，通常根据选择时的事件史选择个体。在这种情况下，确定的主题可能不代表目标人群，分析应考虑到选择机制。本文件有两个目的。第一，研究当数据分析没有根据选择进行调整时会发生什么，第二，提出一个正确的分析。在Andersen-Gill和共享脆弱性回归模型下，我们证明了如果忽略确定性，协变量效应、发病率和脆弱性方差的估计量可能有偏，并且我们证明了通过简单的似然调整，可以获得无偏和一致的估计量。所提出的方法进行评估的模拟研究，并说明了一个数据集，包括复发性气胸。版权所有（c）2016约翰威利父子有限公司
In retrospective studies involving recurrent events, it is common to select individuals based on their event history up to the time of selection. In this case, the ascertained subjects might not be representative for the target population, and the analysis should take the selection mechanism into account. The purpose of this paper is two-fold. First, to study what happens when the data analysis is not adjusted for the selection and second, to propose a corrected analysis. Under the Andersen-Gill and shared frailty regression models, we show that the estimators of covariate effects, incidence, and frailty variance can be biased if the ascertainment is ignored, and we show that with a simple adjustment of the likelihood, unbiased and consistent estimators are obtained. The proposed method is assessed by a simulation study and is illustrated on a data set comprising recurrent pneumothoraces. Copyright (c) 2016 John Wiley & Sons, Ltd.