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：暴露归因分值（AF$_e$），也称为暴露归因风险或超额比例，是指在暴露人群中可通过消除该暴露而避免的疾病病例比例。理解不同暴露的AF$_e$有助于指导公共卫生干预措施。对AF$_e$进行推断的传统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引起的隐藏偏倚敏感。本文提出一种新方法，通过利用病例描述信息来降低对AF$_e$进行统计推断时对隐藏偏倚的敏感性。病例描述信息是指描述病例特征的信息，例如癌症亚型。暴露对某些类型的病例影响可能大于其他类型。我们通过渐近工具（设计敏感性）和模拟研究，探讨了利用病例描述信息如何降低未测量混杂因素导致的偏倚敏感性。我们允许利用病例定义信息可能通过额外的敏感性参数引入新的选择偏倚的可能性。该方法通过重新审视饮酒与绝经后浸润性乳腺癌风险的关系加以说明，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）的病例描述信息进行数据分析。