In observational studies of treatment effects, estimates may be biased by unmeasured confounders, which can potentially affect the validity of the results. Understanding sensitivity to such biases helps assess how unmeasured confounding impacts credibility. The design of an observational study strongly influences its sensitivity to bias. Previous work has shown that the sensitivity to bias can be reduced by dividing a dataset into a planning sample and a larger analysis sample, where the planning sample guides design decisions. But the choice of what fraction of the data to put in the planning sample vs. the analysis sample was ad hoc. Here, we develop an approach to find the optimal fraction using plasmode datasets. We show that our method works well in high-dimensional outcome spaces. We apply our method to study the effects of exposure to second-hand smoke in children. The OptimalSampling R package implementing our method is available at GitHub.

翻译：在治疗效应的观测性研究中，估计值可能受到未测量混杂因素的影响，从而可能影响结果的有效性。理解对此类偏倚的敏感性有助于评估未测量混杂如何影响结论的可信度。观测性研究的设计会显著影响其对偏倚的敏感性。先前的研究表明，通过将数据集划分为规划样本和更大的分析样本（其中规划样本用于指导设计决策），可以降低对偏倚的敏感性。但将多大比例的数据放入规划样本与分析样本的选择是随意的。本文中，我们开发了一种利用拟态数据集寻找最优比例的方法。我们证明了该方法在高维结果空间中表现良好。我们将该方法应用于研究儿童接触二手烟的影响。实现本方法的 OptimalSampling R 包已在 GitHub 上提供。