{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:30:55Z","timestamp":1789475455036,"version":"build-2803163510"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"HIV Prevention Trials Network"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, efficiency gain and applicability of augmented inverse propensity weighted estimators. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability under different randomization schemes, which also motivate a joint calibration strategy using some constructed covariates after applying augmented inverse propensity weighted estimators.<\/jats:p>","DOI":"10.1093\/biomet\/asaf029","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T04:24:16Z","timestamp":1744431856000},"source":"Crossref","is-referenced-by-count":13,"title":["A general form of covariate adjustment in clinical trials under covariate-adaptive randomization"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-5978","authenticated-orcid":false,"given":"Marlena S","family":"Bannick","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Washington , 3980 15th Avenue NE, Box 351617 , Seattle, Washington 698195,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6239-4258","authenticated-orcid":false,"given":"Jun","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Wisconsin-Madison , 1300 University Avenue , Madison, Wisconsin 653706,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Global Statistical Sciences, Eli Lilly and Company , 839 South Delaware Street , Indianapolis, Indiana 46285,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Du","sequence":"additional","affiliation":[{"name":"Global Statistical Sciences, Eli Lilly and Company , 839 South Delaware Street , Indianapolis, Indiana 46285,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1540-1862","authenticated-orcid":false,"given":"Yanyao","family":"Yi","sequence":"additional","affiliation":[{"name":"Global Statistical Sciences, Eli Lilly and Company , 839 South Delaware Street , Indianapolis, Indiana 46285,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6009-641X","authenticated-orcid":false,"given":"Ting","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington , 3980 15th Avenue NE, Box 351617 , Seattle, Washington 698195,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"2025071611262571900_asaf029-B1","doi-asserted-by":"crossref","first-page":"881","DOI":"10.3150\/13-BEJ591","article-title":"On the almost sure convergence of adaptive allocation procedures","volume":"21","author":"Baldi Antognini","year":"2015","journal-title":"Bernoulli"},{"key":"2025071611262571900_asaf029-B2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1257\/aer.p20171038","article-title":"Double\/debiased\/Neyman machine learning of treatment effects","volume":"107","author":"Chernozhukov","year":"2017","journal-title":"Am. 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