Research Center for Genes, Environment and Human Health, College of Public Health, National Taiwan University, Rm, 536, No, 17, Xuzhou Rd,, Taipei 100, Taiwan.
BMC Med Res Methodol. 2014 Feb 5;14:18. doi: 10.1186/1471-2288-14-18.
The randomized controlled study is the gold-standard research method in biomedicine. In contrast, the validity of a (nonrandomized) observational study is often questioned because of unknown/unmeasured factors, which may have confounding and/or effect-modifying potential.
In this paper, the author proposes a perturbation test to detect the bias of unmeasured factors and a perturbation adjustment to correct for such bias. The proposed method circumvents the problem of measuring unknowns by collecting the perturbations of unmeasured factors instead. Specifically, a perturbation is a variable that is readily available (or can be measured easily) and is potentially associated, though perhaps only very weakly, with unmeasured factors. The author conducted extensive computer simulations to provide a proof of concept.
Computer simulations show that, as the number of perturbation variables increases from data mining, the power of the perturbation test increased progressively, up to nearly 100%. In addition, after the perturbation adjustment, the bias decreased progressively, down to nearly 0%.
The data-mining perturbation analysis described here is recommended for use in detecting and correcting the bias of unmeasured factors in observational studies.
随机对照研究是生物医学中的黄金标准研究方法。相比之下，由于未知/未测量的因素，（非随机）观察性研究的有效性经常受到质疑，这些因素可能具有混杂和/或效应修饰的潜力。
在本文中，作者提出了一种通过收集未测量因素的扰动来检测未测量因素偏差的摄动检验和一种修正这种偏差的摄动调整方法。该方法通过收集未测量因素的扰动来规避测量未知因素的问题。具体来说，摄动是一个很容易获得（或可以很容易测量）的变量，并且与未测量因素有潜在的关联，尽管可能只是非常微弱的关联。作者进行了广泛的计算机模拟，以提供一个概念验证。
计算机模拟结果表明，随着用于数据挖掘的摄动变量数量的增加，摄动检验的功效逐渐增加，接近 100%。此外，经过摄动调整后，偏差逐渐减小，接近 0%。
这里描述的数据挖掘摄动分析建议用于检测和修正观察性研究中未测量因素的偏差。
