Work

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

Year: 2025

Type: article

Abstract: The attributable fraction among the exposed (AFe) 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 AFe 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.

Source: Journal of the Royal Statistical Society Series A (Statistics in Society)

DOI: 10.1093/jrsssa/qnaf066

Authors: Kan Chen, Jing Cheng, M Elizabeth Halloran, Dylan S Small

Institutions: Harvard University, University of Pennsylvania, University of California, San Francisco, Fred Hutch Cancer Center

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Aging