Using case description information to reduce sensitivity to bias for the attributable fraction among the exposed.

Abstract

The attributable fraction among the exposed ( ${AF}_e$ ) is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. In this article, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the ${AF}_e$ by leveraging case description information such as subtype of cancer. The proposed method is examined through an asymptotic tool, design sensitivity, simulation studies, and case studies of alcohol consumption and the risk of postmenopausal invasive breast cancer utilizing information on the subtype of cancer using data from the Women's Health Initiative Observational Study allowing the possibility that leveraging case definition information may introduce selection bias through an additional sensitivity parameter.