Inverse probability weighting for covariate adjustment in randomized studies.
Inverse probability weighting for covariate adjustment in randomized studies.
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随机研究中协变量调整的逆概率加权。
DOI：
10.1002/sim.5969
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发表时间：
2014
影响因子：
2
通讯作者：
Li,Lingling
中科院分区：
文献类型：
--
作者：
Shen,Changyu;Li,Xiaochun;Li,Lingling
随机化临床试验中的协变量调整具有获得精确度的潜在益处。它也有降低客观性的潜在陷阱，因为它打开了选择一个“有利”的模型，产生强大的治疗效益估计的可能性。虽然有大量的统计文献针对第一个方面，现实的解决方案，以加强客观推断和提高精度是罕见的。由于一项典型的随机试验需要适应统计学考虑之外的许多实施问题，因此保持客观性至少与精度增益一样重要，如果不是更重要的话，特别是从监管机构的角度来看。在这篇文章中，我们提出了一个基于逆概率加权的两阶段估计过程，以在不影响客观性的情况下实现更好的精度。该程序的设计方式是在看到结果之前进行协变量调整，有效地减少了选择一个“有利”的模型，产生强大的干预效果的可能性。估计过程的理论和数值特性。应用所提出的方法的一个真实的数据的例子。版权所有© 2013约翰威利父子有限公司.
Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a ‘favorable’ model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two‐stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a ‘favorable’ model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented. Copyright © 2013 John Wiley & Sons, Ltd.