The attributable fraction among the exposed (\textbf{AF}eβ), 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}eβ for different exposures helps guide
public health interventions. The conventional approach to inference for the
\textbf{AF}eβ 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}eβ 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).Comment: 30 pages, 8 tables, 1 figur