2020/05/06 by Louisa H. Smith, Maya B. Mathur, Tyler J. VanderWeele
Environmental Science · Mathematics · #Advanced Causal Inference Techniques #Confounding #Health, Environment, Cognitive Aging #Information bias #Range (aeronautics) #Selection (genetic algorithm) #Selection bias #Sensitivity (control systems) #Statistical Methods and Bayesian Inference #Upper and lower bounds #Variety (cybernetics) #stat.ME
paper · pdf · doi:10.1097/ede.0000000000001380
23 pages (main text); 19 pages (appendix)
arxiv created 2020/05/06 · openalex created_date 2020/05/13 · openalex publication_date 2021/06/24 · arxiv updated 2021/08/11 · openalex updated_date 2026/08/05
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, although 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 owing 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. We apply this technique to a study where unmeasured confounding and selection bias are both concerns and to another study in which possible differential exposure misclassification and confounding are concerns. The approach we describe, though conservative, is easier to implement and makes simpler assumptions than quantitative bias analysis. We provide R functions to aid implementation.