Use of sensitivity analyses to assess uncontrolled confounding from unmeasured variables in observational, active comparator pharmacoepidemiologic studies: a systematic review
Year: 2024
Type: review
Study design: Systematic Review
Abstract: Understanding the potential for, and direction and magnitude of uncontrolled confounding is critical for generating informative real-world evidence. Many sensitivity analyses are available to assess robustness of study results to residual confounding, but it is unclear how researchers are using these methods. We conducted a systematic review of published active-comparator cohort studies of drugs or biologics to summarize use of sensitivity analyses aimed at assessing uncontrolled confounding from an unmeasured variable. We reviewed articles in 5 medical and 7 epidemiologic journals published between January 1, 2017, and June 30, 2022. We identified 158 active-comparator cohort studies: 76 from medical and 82 from epidemiologic journals. Residual, unmeasured, or uncontrolled confounding was noted as a potential concern in 93% of studies, but only 84 (53%) implemented at least 1 sensitivity analysis to assess uncontrolled confounding from an unmeasured variable. The most common analyses were E-values among medical journal articles (21%) and restriction on measured variables among epidemiologic journal articles (22%). Researchers must rigorously consider the role of residual confounding in their analyses and the best sensitivity analyses for assessing this potential bias. This article is part of a Special Collection on Pharmacoepidemiology.
Source: American Journal of Epidemiology
DOI: 10.1093/aje/kwae234
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funders: National Institutes of Health, U.S. Food and Drug Administration, Agency for Healthcare Research and Quality, University of North Carolina at Chapel Hill, Food and Drug Administration, National Institute of Allergy and Infectious Diseases, Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill
OpenAlex ID: W4401346884
