The Importance of Making Assumptions in Bias Analysis

• Type of Material: Article
• Authors: Richard F MacLehose, University of Minnesota, Minneapolis; Thomas P Ahern, University of Vermont; Timothy Lash, Emory University; Charles Poole, University of North Carolina; Sander Greenland, University of California Los Angeles
• Language: English
• Date: 2021-09-01
• Publisher: LIPPINCOTT WILLIAMS & WILKINS
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1097/EDE.0000000000001381
• Title of Journal or Parent Work: EPIDEMIOLOGY
• Volume: 32
• Issue: 5
• Start Page: 617
• End Page: 624
• Grant/Funding Information: This work was supported by the US National Library of Medicine (R01LM013049).
• Keywords: Quantitative bias analysis; SMOKING; Public, Environmental & Occupational Health; SENSITIVITY-ANALYSIS; Science & Technology; FORMULAS; Uncontrolled confounding; Life Sciences & Biomedicine; Unmeasured confounding; E-value
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Epidemiology

Abstract

Quantitative bias analyses allow researchers to adjust for uncontrolled confounding, given specification of certain bias parameters. When researchers are concerned about unknown confounders, plausible values for these bias parameters will be difficult to specify. Ding and VanderWeele developed bounding factor and E-value approaches that require the user to specify only some of the bias parameters. We describe the mathematical meaning of bounding factors and E-values and the plausibility of these methods in an applied context. We encourage researchers to pay particular attention to the assumption made, when using E-values, that the prevalence of the uncontrolled confounder among the exposed is 100% (or, equivalently, the prevalence of the exposure among those without the confounder is 0%). We contrast methods that attempt to bound biases or effects and alternative approaches such as quantitative bias analysis. We provide an example where failure to make this distinction led to erroneous statements. If the primary concern in an analysis is with known but unmeasured potential confounders, then E-values are not needed and may be misleading. In cases where the concern is with unknown confounders, the E-value assumption of an extreme possible prevalence of the confounder limits its practical utility.