Sensitivity to unmeasured confounding is not typically a primary consideration in designing treated-control comparisons in observational studies. We introduce a framework allowing researchers to optimize robustness to omitted variable bias at the design stage using a measure called design sensitivity. Design sensitivity, which describes the asymptotic power of a sensitivity analysis, allows transparent assessment of the impact of different estimation strategies on sensitivity. We apply this general framework to two commonly-used sensitivity models, the marginal sensitivity model and the variance-based sensitivity model. By comparing design sensitivities, we interrogate how key features of weighted designs, including choices about trimming of weights and model augmentation, impact robustness to unmeasured confounding, and how these impacts may differ for the two different sensitivity models. We illustrate the proposed framework on a study examining drivers of support for the 2016 Colombian peace agreement.

翻译：未测量的混杂因素敏感性通常不是观察性研究中处理-对照比较设计时的主要考虑因素。我们引入了一个框架，允许研究人员在设计阶段使用一种称为设计敏感性的指标来优化对遗漏变量偏差的稳健性。设计敏感性描述了敏感性分析的渐近检验效能，能够透明地评估不同估计策略对敏感性的影响。我们将这一通用框架应用于两种常用的敏感性模型：边际敏感性模型和基于方差的敏感性模型。通过比较设计敏感性，我们探究了加权设计的关键特征（包括权重修剪和模型增强的选择）如何影响对未测量混杂因素的稳健性，以及这些影响在两种不同敏感性模型中可能存在的差异。我们通过一项关于2016年哥伦比亚和平协议支持驱动因素的研究来说明所提出的框架。