Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single‐point exposure

• Published in: Statistics in medicine Vol. 42; no. 4; pp. 487 - 516
• Main Authors: Varga, Anita Natalia, Guevara Morel, Alejandra Elizabeth, Lokkerbol, Joran, Dongen, Johanna Maria, Tulder, Maurits Willem, Bosmans, Judith Ekkina
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.02.2023; Wiley Subscription Services, Inc
• Subjects: Bias; causal inference; Computer Simulation; confounding; Humans; Observational studies; observational study; Review; Simulation; treatment effect; confounding; treatment effect; causal inference; simulation; bias; observational study
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9628

The aim of this article was to perform a scoping review of methods available for dealing with confounding when analyzing the effect of health care treatments with single‐point exposure in observational data. We aim to provide an overview of methods and their performance assessed by simulation studies indexed in PubMed. We searched PubMed for simulation studies published until January 2021. Our search was restricted to studies evaluating binary treatments and binary and/or continuous outcomes. Information was extracted on the methods' assumptions, performance, and technical properties. Of 28,548 identified references, 127 studies were eligible for inclusion. Of them, 84 assessed 14 different methods (ie, groups of estimators that share assumptions and implementation) for dealing with measured confounding, and 43 assessed 10 different methods for dealing with unmeasured confounding. Results suggest that there are large differences in performance between methods and that the performance of a specific method is highly dependent on the estimator. Furthermore, the methods' assumptions regarding the specific data features also substantially influence the methods' performance. Finally, the methods result in different estimands (ie, target of inference), which can even vary within methods. In conclusion, when choosing a method to adjust for measured or unmeasured confounding it is important to choose the most appropriate estimand, while considering the population of interest, data structure, and whether the plausibility of the methods' required assumptions hold.