Measurement and Quantitative Methods, Education; Agriculture and Natural Resources, Michigan State University, East Lansing, MI.
Center for Spatial Data Science, University of Chicago, Chicago IL.
J Clin Epidemiol. 2021 Jun;134:150-159. doi: 10.1016/j.jclinepi.2021.01.025. Epub 2021 Mar 15.
We apply a general case replacement framework for quantifying the robustness of causal inferences to characterize the uncertainty of findings from clinical trials.
We express the robustness of inferences as the amount of data that must be replaced to change the conclusion and relate this to the fragility of trial results used for dichotomous outcomes. We illustrate our approach in the context of an RCT of hydroxychloroquine on pneumonia in COVID-19 patients and a cumulative meta-analysis of the effect of antihypertensive treatments on stroke.
We developed the Robustness of an Inference to Replacement (RIR), which quantifies how many treatment cases with positive outcomes would have to be replaced with hypothetical patients who did not receive a treatment to change an inference. The RIR addresses known limitations of the Fragility Index by accounting for the observed rates of outcomes. It can be used for varying thresholds for inference, including clinical importance.
Because the RIR expresses uncertainty in terms of patient experiences, it is more relatable to stakeholders than P-values alone. It helps identify when results are statistically significant, but conclusions are not robust, while considering the rareness of events in the underlying data.
我们应用一般病例替换框架来量化因果推断的稳健性，以描述临床试验结果的不确定性。
我们将推断的稳健性表示为必须替换的数据量，以改变结论，并将其与用于二分类结果的试验结果的脆弱性联系起来。我们在羟氯喹治疗 COVID-19 肺炎的 RCT 和降压治疗对卒中影响的累积荟萃分析的背景下说明了我们的方法。
我们开发了对替换的推断稳健性（RIR），它量化了需要用假设的未接受治疗的患者替换多少阳性结果的治疗病例才能改变推断。RIR 通过考虑观察到的结局发生率来解决脆弱性指数的已知局限性。它可用于不同的推断阈值，包括临床重要性。
因为 RIR 用患者体验来表示不确定性，所以它比 P 值更能与利益相关者相关。它有助于确定结果具有统计学意义，但结论不稳健的情况，同时考虑到基础数据中事件的罕见性。
