Constructing inverse probability weights for marginal structural models
Constructing inverse probability weights for marginal structural models
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DOI：
10.1093/aje/kwn164
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发表时间：
2008-09-15
影响因子：
5
通讯作者：
Hernan, Miguel A.
中科院分区：
文献类型：
--
作者：
Cole, Stephen R.;Hernan, Miguel A.
逆概率加权法（以下简称加权法）可用于在一致性、互换性、阳性和用于估计权重的模型无错误设定这四个假设下调整测量的混杂和选择偏倚。近年来，一些已发表的估计的影响，随时间变化的曝光已基于加权估计的边际结构模型的参数，因为，不同于标准的统计方法，加权可以适当地调整测量的随时间变化的混杂因素影响的先前曝光。作为一个例子，作者描述了最后三个假设，使用病毒载量的变化，由于开始抗逆转录病毒治疗的918人类免疫缺陷病毒感染的美国男性和女性，随访时间为1996年至2005年的中位数为5.8年。作者描述了流行病学家在试图做出推论时可能遇到的权衡。例如，偏差和精度之间的权衡被示出为混杂被控制的程度的函数。权重截断是一种非正式的，易于实现的方法来处理这些权衡。逆概率加权提供了一个强大的方法工具，可以揭示暴露的因果影响，否则被掩盖。然而，与所有方法一样，诊断和敏感性分析对于正确使用至关重要。
The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use.