In this article, we introduce the evalue package, which performs sensitivity analyses for unmeasured confounding in observational studies using the methodology proposed by VanderWeele and Ding (2017, Annals of Internal Medicine 167: 268–274). evalue reports E-values, defined as the minimum strength of association on the risk-ratio scale that an unmeasured confounder would need to have with both the treatment assignment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. evalue computes E-values for point estimates (and optionally, confidence limits) for several common outcome types, including risk and rate ratios, odds ratios with common or rare outcomes, hazard ratios with common or rare outcomes, standardized mean differences in outcomes, and risk differences.

在本文中，我们介绍了评估工具包，该工具包使用VanderWeele和Ding（2017，内科学年鉴167：268-274）提出的方法对观察研究中未测混杂因素进行敏感性分析。evalue报告E值，定义为未衡量的混杂因素与治疗分配和结果之间必须具有的风险比范围上的最小关联强度，以完全解释特定的治疗结果关联，具体取决于所测协变量 评估 计算几种常见结局类型的点估计值（以及可选的置信度限制）的E值，包括风险和比率，具有常见或罕见结局的比值比，具有常见或罕见结局的危险比，标准化的结果均值差和风险差异。