The attributable fraction among the exposed (\textbf{AF}$_e$), also known as the attributable risk or excess fraction among the exposed, is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. Understanding the \textbf{AF}$_e$ for different exposures helps guide public health interventions. The conventional approach to inference for the \textbf{AF}$_e$ assumes no unmeasured confounding and could be sensitive to hidden bias from unobserved covariates. In this paper, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the \textbf{AF}$_e$ by leveraging case description information. Case description information is information that describes the case, e.g., the subtype of cancer. The exposure may have more of an effect on some types of cases than other types. We explore how leveraging case description information can reduce sensitivity to bias from unmeasured confounding through an asymptotic tool, design sensitivity, and simulation studies. We allow for the possibility that leveraging case definition information may introduce additional selection bias through an additional sensitivity parameter. The proposed methodology is illustrated by re-examining alcohol consumption and the risk of postmenopausal invasive breast cancer using case description information on the subtype of cancer (hormone-sensitive or insensitive) using data from the Women's Health Initiative (WHI) Observational Study (OS).

翻译：暴露组归因分值（\textbf{AF}$_e$），亦称暴露归因风险或超额风险，是指暴露群体中通过消除暴露可避免的疾病病例比例。理解不同暴露的\textbf{AF}$_e$有助于指导公共卫生干预措施。针对\textbf{AF}$_e$的传统推断方法假设不存在未测量的混杂因素，因此可能对未观测协变量导致的潜在偏倚敏感。本文提出一种新方法，通过利用病例描述信息来降低统计推断\textbf{AF}$_e$时对隐伏偏倚的敏感性。病例描述信息是指描述病例特征的信息，如癌症亚型。暴露可能对某些病例类型的影响大于其他类型。我们通过渐进工具（设计灵敏度）和模拟研究，探索如何利用病例描述信息降低未测量混杂因素导致的偏倚敏感性。同时，我们允许通过引入额外灵敏度参数的方式，考虑利用病例定义信息可能引入的选择偏倚。通过重新分析酒精摄入与绝经后浸润性乳腺癌风险的关系，利用妇女健康倡议（WHI）观察性研究（OS）中关于癌症亚型（激素敏感型或不敏感型）的病例描述信息，对提出的方法进行了实例验证。