论文标题
情境化电子价值，以解释对无法衡量的混杂分析的敏感性
Contextualizing E-values for Interpretable Sensitivity to Unmeasured Confounding Analyses
论文作者
论文摘要
流行病学和观察性研究提供的证据强度固有地受到无法衡量的混淆的潜力的限制。研究人员应提出对未衡量的混杂分析的量化敏感性，该分析与研究的观察到的协变量相关。 Vanderweele和Ding的电子价值为假设未计算的混杂的大小提供了一个易于计算的度量，以使研究的结果尚无定论。我们提出了观察到的协变量电子价值，以将敏感性分析“假设的电子价值单独或组内的实际影响”背景相关化。我们介绍了一个灵敏度分析图，该图在电子价值量表上介绍了观察到的协变量电子价值，即其相应的观察到的偏置效应，这是研究结果的原始量表。该观察到的偏置图可以轻松比较假设的电子价值，观察到的协变量电子价值和观察到的偏见效应。我们用特定示例说明了这些方法，并提供了一个补充附录，其中包含可修改的代码，该代码教授如何实现该方法并创建出版物质量数字。
The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. Researchers should present a quantified sensitivity to unmeasured confounding analysis that is contextualized by the study's observed covariates. VanderWeele and Ding's E-value provides an easily calculated metric for the magnitude of the hypothetical unmeasured confounding required to render the study's result inconclusive. We propose the Observed Covariate E-value to contextualize the sensitivity analysis' hypothetical E-value within the actual impact of observed covariates, individually or within groups. We introduce a sensitivity analysis figure that presents the Observed Covariate E-values, on the E-value scale, next to their corresponding observed bias effects, on the original scale of the study results. This observed bias plot allows easy comparison of the hypothetical E-values, Observed Covariate E-values, and observed bias effects. We illustrate the methods with a specific example and provide a supplemental appendix with modifiable code that teaches how to implement the method and create a publication quality figure.
