{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:35:52Z","timestamp":1782401752562,"version":"3.54.5"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","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":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>High\u2010dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)\u2010hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We compared SL\u2010hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically\u2010identified covariates, using a cohort study comparing real\u2010world bleeding rates between ibrutinib\u2010 and bendamustine\u2010rituximab (BR)\u2010treated individuals with chronic lymphocytic leukemia in Optum's de\u2010identified Clinformatics\u00ae Data Mart commercial claims database (2013\u20132020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically\u2010identified covariate balance (absolute standardized difference [ASD] thresholds of &lt;0.10 and &lt;0.05) and outcome HR precision (95% confidence intervals).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified 2423 ibrutinib\u2010 and 1102 BR\u2010treated individuals. Including &gt;200 empirically\u2010identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL\u2010hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL\u2010hdPS than with individual hdPS models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In a real\u2010world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.5678","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T07:50:39Z","timestamp":1692777039000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Combining <scp>Super Learner<\/scp> with high\u2010dimensional propensity score to improve confounding adjustment: A real\u2010world application in chronic lymphocytic leukemia"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6305-256X","authenticated-orcid":false,"given":"Neil","family":"Dhopeshwarkar","sequence":"first","affiliation":[{"name":"Center for Real\u2010World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Real\u2010World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Hennessy","sequence":"additional","affiliation":[{"name":"Center for Real\u2010World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna M.","family":"Rhodes","sequence":"additional","affiliation":[{"name":"Division of Hematology\/Medical Oncology, Department of Medicine Northwell Health  New Hyde Park New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Cuker","sequence":"additional","affiliation":[{"name":"Department of Medicine, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Pathology and Laboratory Medicine, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles E.","family":"Leonard","sequence":"additional","affiliation":[{"name":"Center for Real\u2010World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania 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