Multiple-Bias Sensitivity Analysis Using Bounds

• Activity Number: 106 - New Frontiers and Developments in Causal Inference
• Type: Invited
• Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Louisa H. Smith* and Maya Mathur and Tyler J VanderWeele
• Companies: Harvard T.H. Chan School of Public Health and Stanford University and Harvard University
• Keywords: bias analysis; sensitivity analysis; causal inference; epidemiology; bounds

Abstract

Unmeasured confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, while more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate -- or overestimate -- their joint impact. We show that it is possible to bound the total composite bias due to these three sources, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. The approach we describe is easy to implement with minimal assumptions, and we provide R functions to do so.