Methods for dealing with time-dependent confounding
Methods for dealing with time-dependent confounding
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DOI：
10.1002/sim.5686
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发表时间：
2013-04-30
影响因子：
2
通讯作者：
Sterne, J. A. C.
中科院分区：
文献类型：
--
作者：
Daniel, R. M.;Cousens, S. N.;Sterne, J. A. C.
纵向研究，即在一段时间内重复收集受试者的数据，在医学研究中很常见。当估计时变治疗或暴露对稍后测量的关注结果的影响时，如果这些混杂因素本身受到治疗的影响，则标准方法无法在存在时变混杂因素的情况下给出一致的估计值。罗宾斯和他的同事们提出了几种替代方法，只要某些假设成立，就可以避免与标准方法相关的问题。它们包括g-计算公式、边际结构模型的逆概率加权估计和结构嵌套模型的g-估计。在本教程中，我们将介绍这些方法中的每一种，探索它们之间的联系和差异，以及在不同设置中选择一种方法的原因。版权所有（c）2012约翰威利父子有限公司
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in medical research. When estimating the effect of a time-varying treatment or exposure on an outcome of interest measured at a later time, standard methods fail to give consistent estimators in the presence of time-varying confounders if those confounders are themselves affected by the treatment. Robins and colleagues have proposed several alternative methods that, provided certain assumptions hold, avoid the problems associated with standard approaches. They include the g-computation formula, inverse probability weighted estimation of marginal structural models and g-estimation of structural nested models. In this tutorial, we give a description of each of these methods, exploring the links and differences between them and the reasons for choosing one over the others in different settings. Copyright (c) 2012 John Wiley & Sons, Ltd.