Quantum Health, Columbus, OH 43235, U.S.A.
Stat Med. 2013 Sep 10;32(20):3552-68. doi: 10.1002/sim.5802. Epub 2013 Apr 1.
Although randomized controlled trials are considered the 'gold standard' for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is unknown whether estimators of effect size are biased by excluding a portion of the target population from enrollment. We propose to use observational data to estimate the bias due to enrollment restrictions, which we term generalizability bias. In this paper, we introduce a class of estimators for the generalizability bias and use simulation to study its properties in the presence of non-constant treatment effects. We find the surprising result that our estimators can be unbiased for the true generalizability bias even when all potentially confounding variables are not measured. In addition, our proposed doubly robust estimator performs well even for mis-specified models.
虽然随机对照试验被认为是临床研究的“金标准”，但排除标准的使用可能会影响结果的外部有效性。目前尚不清楚排除目标人群的一部分是否会影响效应大小估计值。我们建议使用观察数据来估计由于纳入限制而产生的偏差，我们称之为可推广性偏差。在本文中，我们提出了一类用于可推广性偏差的估计量，并通过模拟研究了在治疗效果非常数的情况下其性质。我们发现了一个令人惊讶的结果，即使没有测量所有潜在的混杂变量，我们的估计量也可以对真实的可推广性偏差进行无偏估计。此外，我们提出的双重稳健估计量即使在模型指定不当的情况下也能很好地发挥作用。
