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When and Why to Use Overlap Weighting: Clarifying Its Role, Assumptions, and Estimand in Real-World Studies.
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Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, Maryland, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States of America. Electronic address: john.rizk@u
Objective: To examine the strengths and limitations of overlap weighting in observational studies, and to clarify when it is appropriate to use this method based on the target estimand.
Study Design And Setting: This is a narrative commentary that reviews recent methodological developments and real-world examples to highlight how overlap weighting operates, when it provides advantages over methods like IPTW, and the importance of aligning analytic methods with the causal question and estimand.
Results: Overlap weighting produces bounded, stable weights and achieves exact mean covariate balance in the subset of patients with overlapping treatment probabilities near 0.
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Introduction: Overlap weighting has emerged as a valuable method for addressing confounding in observational studies, particularly in real-world data settings characterized by imbalanced covariates and limited overlap between treatment groups. Its ability to produce stable, interpretable estimates makes it an attractive alternative to inverse probability of treatment weighting (IPTW), which can suffer from extreme weights and instability.
Areas Covered: This report outlines the methodological basis of overlap weighting and contrasts it with IPTW.
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