{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:25:49Z","timestamp":1787185549403,"version":"build-2736575974"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":50,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Work-Life Balance in Academia: A Career Skills Course for Researchers"},{"name":"Center for Coproduction of Inclusion"},{"name":"Diversity and Equity"},{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In randomized controlled trials, covariate adjustment can improve statistical power and reduce the required sample size compared with unadjusted estimators. Analysis of covariance (ANCOVA) is often used to adjust for baseline covariates when outcomes are continuous. To design a sample size based on ANCOVA, it is necessary to prespecify the association between the outcome and baseline covariates, as well as among the baseline covariates themselves. However, determining these parameters at the design stage is challenging. Although it may be possible to adaptively assess these during the trial and recalculate the required sample size, existing sample size recalculation methods assume that the joint distribution of the outcome and baseline covariates is multivariate normal, which is not always the case in practice. In this study, we propose a blinded sample size recalculation method for the ANCOVA estimator based on the asymptotic relative efficiency under minimal distributional assumptions, thus accommodating arbitrary model misspecification. The proposed method is able to achieve the nominal power and reduce the required sample size without inflating the type I error rate in the final analysis. We conducted simulations to evaluate the performance of the proposed method under various scenarios. Applied to an HIV clinical trial, the proposed method reduced the required sample size by up to one-third compared to the unadjusted analysis, despite occasional slight increases.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag147","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T11:55:14Z","timestamp":1786017314000},"source":"Crossref","is-referenced-by-count":0,"title":["Blinded sample size recalculation in randomized controlled trials with analysis of covariance"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7163-7213","authenticated-orcid":false,"given":"Takumi","family":"Kanata","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033 ,","place":["Japan"]},{"name":"Department of Clinical Data Science, Clinical Research & Education Promotion Division, National Center of Neurology and Psychiatry , Tokyo 187-8551 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3792-5368","authenticated-orcid":false,"given":"Yasuhiro","family":"Hagiwara","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Oba","sequence":"additional","affiliation":[{"name":"Interfaculty Initiative in Information Studies, The University of Tokyo , Tokyo 113-0033 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"2026081920012882400_bib1","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1111\/biom.13184","article-title":"Robustness of ANCOVA in randomized trials with unequal randomization","volume":"76","author":"Bartlett","year":"2020","journal-title":"Biometrics"},{"key":"2026081920012882400_bib2","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1111\/biom.13377","article-title":"Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes","volume":"77","author":"Benkeser","year":"2021","journal-title":"Biometrics"},{"key":"2026081920012882400_bib3","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.jclinepi.2007.02.006","article-title":"A simple sample size formula for analysis of covariance in randomized clinical trials","volume":"60","author":"Borm","year":"2007","journal-title":"Journal of Clinical Epidemiology"},{"key":"2026081920012882400_bib4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1081\/BIP-100101018","article-title":"Estimation following extension of a study on the basis of conditional power","volume":"10","author":"Denne","year":"2000","journal-title":"Journal of Biopharmaceutical Statistics"},{"key":"2026081920012882400_bib5","article-title":"Guideline on adjustment for baseline covariates in clinical trials","author":"European Medicines Agency","year":"2015"},{"key":"2026081920012882400_bib6","article-title":"Adjusting for covariates in randomized clinical trials for drugs and biological products guidance for industry","author":"Food and Drug Administration","year":"2023"},{"key":"2026081920012882400_bib7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1002\/pst.398","article-title":"Blinded sample size recalculation for clinical trials with normal data and baseline adjusted analysis","volume":"10","author":"Friede","year":"2011","journal-title":"Pharmaceutical Statistics"},{"key":"2026081920012882400_bib8","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1002\/sim.4780140922","article-title":"Planning and revising the sample size for a trial","volume":"14","author":"Gould","year":"1995","journal-title":"Statistics in Medicine"},{"key":"2026081920012882400_bib9","first-page":"243","article-title":"Sample size formulas for normal theory T tests","volume":"35","author":"Guenther","year":"1981","journal-title":"The American Statistican"},{"key":"2026081920012882400_bib10","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1056\/NEJM199610103351501","article-title":"A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. 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