Experimental and observational studies often lead to spurious association between the outcome and independent variables describing the intervention, because of confounding to third-party factors. Even in randomized clinical trials, confounding might be unavoidable due to small sample sizes. Practically, this poses a problem, because it is either expensive to re-design and conduct a new study or even impossible to alleviate the contribution of some confounders due to e.g. ethical concerns. Here, we propose a method to consistently derive hypothetical studies that retain as many of the dependencies in the original study as mathematically possible, while removing any association of observed confounders to the independent variables. Using historic studies, we illustrate how the confounding-free scenario re-estimates the effect size of the intervention. The new effect size estimate represents a concise prediction in the hypothetical scenario which paves a way from the original data towards the design of future studies.

翻译：实验研究和观察性研究常因第三方混杂因素而导致结果与描述干预的自变量之间产生虚假关联。即使在随机对照试验中，小样本量也可能使混杂因素无法避免。这在实际中构成问题，因为重新设计并开展新研究成本高昂，甚至因伦理限制等原因而无法减轻某些混杂因素的影响。本研究提出一种方法，可系统推导出在数学上尽可能保留原始研究中依赖关系、同时消除观测到的混杂因素与自变量之间关联的假设研究。通过历史研究案例，我们展示了无混杂场景如何重新估计干预效应量。新的效应量估计值代表了假设场景中的精确预测，为从原始数据走向未来研究设计开辟了路径。