{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T02:14:44Z","timestamp":1793499284130,"version":"4.5.0"},"reference-count":28,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100002515","name":"Korea National Open University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Inverse probability weighting (IPW) is a widely used method to estimate the causal effect of treatment from observational data. However, it can be unstable when extreme propensity score (PS) values lead to very large weights. Overlap weights (OW), which emphasize subjects in areas of covariate overlap, reduce the influence of extreme PS without excluding participants. While the OW method has shown strong performance in simulations with continuous outcomes, its utility in binary outcome settings\u2014common in health research\u2014has not been thoroughly evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted simulation studies to evaluate the performance of OW in comparison to other PS weighting methods including IPW, trimmed IPW, and matching weights, in settings with extreme PS values and a binary outcome. Using simulated datasets with varying degrees of PS overlap and treatment prevalence, we assessed covariate balance and treatment effect estimation performance. The performance of the PS weighting methods was further illustrated through an application to data from a study on pancreatic ductal adenocarcinoma.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In simulation studies, IPW's performance deteriorated markedly as the overlap in the covariate distribution decreased. In contrast, OW achieved exact covariate balance and consistently showed the highest efficiency among all methods evaluated. In the application to real\u2010world data characterized by low treatment prevalence and substantial covariate imbalance, OW also outperformed the other methods in terms of both standard error and covariate balance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest superior performance of OW in terms of covariate balance and estimation efficiency in settings with extreme PS and a binary outcome.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.70253","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:04:56Z","timestamp":1761894296000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Overlap Weights for Binary Outcomes: A Performance Assessment"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2702-1536","authenticated-orcid":false,"given":"Seo Young","family":"Park","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science Korea National Open University  Seoul South\u00a0Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeil","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Bioinformatics, and Biomathematics Georgetown University  Washington, DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Hoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Hepato\u2010Biliary and Pancreatic Surgery, Department of Surgery Asan Medical Center, University of Ulsan College of Medicine  Seoul South\u00a0Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaewoo","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Surgery Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine  Seoul South\u00a0Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9497-0574","authenticated-orcid":false,"given":"Hana","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Drug Evaluation and Research United States Food and Drug Administration  Silver Spring Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6607"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2217\/cer-2020-0013"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/000313004X5824"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwn164"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-STS313"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018174"},{"issue":"1","key":"e_1_2_9_9_1","first-page":"250","article-title":"Addressing Extreme Propensity Scores via the Overlap Weights","volume":"188","author":"Li F.","year":"2019","journal-title":"American Journal of 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