Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding
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真实效应还是偏差？用未测混杂定量敏感性分析评估真实世界证据
- 研究问题
- 面对无法直接验证的无未测混杂假设，研究者如何在设计和分析阶段评估真实世界因果结论对潜在混杂的稳健性？
- 方法与思路
- 作者提出分阶段的最佳实践指导、框架问题与分析工具箱，并用基于纤维肌痛研究的模拟数据展示多种敏感性方法。
- 材料陈述的贡献
- 摘要提供从设计审查、补充数据到量化结论稳健性的指导，旨在降低未测混杂敏感性分析的实际使用门槛。
- 阅读关注点
- 应核对各工具针对的混杂机制、所需输入与适用条件，以及敏感性参数的解释和不同方法结果之间的可比性。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审与期刊接收未核实；示例采用模拟数据，摘要未提供各方法细节或证明混杂不存在。
原文摘要
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.