The assumption of no unmeasured confounders is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains underutilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements required for application of each method. With the advent of sensitivity analyses methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder, along with publicly available code for implementation, roadblocks toward broader use are decreasing. To spur greater application, here we present a best practice guidance to address the potential for unmeasured confounding at both the design and analysis stages, including a set of framing questions and an analytic toolbox for researchers. The questions at the design stage guide the research through steps evaluating the potential robustness of the design while encouraging gathering of additional data to reduce uncertainty due to potential confounding. At the analysis stage, the questions guide researchers to quantifying the robustness of the observed result and providing researchers with a clearer indication of the robustness of their conclusions. We demonstrate the application of the guidance using simulated data based on a real-world fibromyalgia study, applying multiple methods from our analytic toolbox for illustration purposes.

翻译：无未测量混杂因素假设是因果推断所需的关键但不可验证的假设，然而评估真实世界证据稳健性的定量敏感性分析仍未被充分利用。其使用不足的部分原因可能在于实施复杂性，以及每种方法应用所需的具体且严格的数据要求。随着广泛适用（无需识别特定未测量混杂因素）的敏感性分析方法的发展，以及公开可用代码的实施，广泛使用的障碍正在减少。为促进更广泛的应用，我们在此提出一套最佳实践指南，用于在设计阶段和分析阶段应对潜在的未测量混杂，包括一组框架性问题及面向研究人员的分析工具包。设计阶段的问题引导研究者通过步骤评估设计潜在稳健性，同时鼓励收集额外数据以降低潜在混杂引起的不确定性。分析阶段的问题引导研究者量化观察结果的稳健性，并为其结论的稳健性提供更清晰的指示。我们基于一项真实纤维肌痛研究的模拟数据演示了该指南的应用，并通过分析工具包中的多种方法进行示例说明。