{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:56:13Z","timestamp":1768409773180,"version":"3.49.0"},"reference-count":46,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T00:00:00Z","timestamp":1575936000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"publisher","award":["HHSF223201400030I"],"award-info":[{"award-number":["HHSF223201400030I"]}],"id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR002319"],"award-info":[{"award-number":["UL1 TR002319"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2020,2,20]]},"abstract":"<jats:p>We consider the critical problem of pharmacosurveillance for adverse events once a drug or medical product is incorporated into routine clinical care. When making inference on comparative safety using large\u2010scale electronic health records, we often encounter an extremely rare binary adverse outcome with a large number of potential confounders. In this context, it is challenging to offer flexible methods to adjust for high\u2010dimensional confounders, whereas use of the propensity score (PS) can help address this challenge by providing both confounding control and dimension reduction. Among PS methods, regression adjustment using the PS as a covariate in an outcome model has been incompletely studied and potentially misused. Previous studies have suggested that simple linear adjustment may not provide sufficient control of confounding. Moreover, no formal representation of the statistical procedure and associated inference has been detailed. In this paper, we characterize a three\u2010step procedure, which performs flexible regression adjustment of the estimated PS followed by standardization to estimate the causal effect in a select population. We also propose a simple variance estimation method for performing inference. Through a realistic simulation mimicking data from the Food and Drugs Administration's Sentinel Initiative comparing the effect of angiotensin\u2010converting enzyme inhibitors and beta blockers on incidence of angioedema, we show that flexible regression on the PS resulted in less bias without loss of efficiency, and can outperform other methods when the PS model is correctly specified. In addition, the direct variance estimation method is a computationally fast and reliable approach for inference.<\/jats:p>","DOI":"10.1002\/sim.8410","type":"journal-article","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T05:45:37Z","timestamp":1576043137000},"page":"369-386","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8566-9552","authenticated-orcid":false,"given":"Xu","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Biostatistics University of Michigan Ann Arbor Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Wellman","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington Seattle Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick J.","family":"Heagerty","sequence":"additional","affiliation":[{"name":"Biostatistics Unit Kaiser Permanente Washington Health Research Institute Seattle Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer C.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington Seattle Washington"},{"name":"Biostatistics Unit Kaiser Permanente Washington Health Research Institute Seattle Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea J.","family":"Cook","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington Seattle Washington"},{"name":"Biostatistics Unit Kaiser Permanente Washington Health Research Institute Seattle Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1014427"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1017\/CCOL0521382483"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/0270-0255(86)90088-6"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq472"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq474"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2618"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2781"},{"key":"e_1_2_9_10_1","first-page":"920","article-title":"Comment on \u201cInference for semiparametric models: Some questions and an answer\u201d by P.J. 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