{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:30:08Z","timestamp":1790098208526,"version":"4.0.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed outcomes, allowing for the application of flexible statistical learning tools to estimate the nuisance functions. We propose an analytic Wald-type confidence interval and a double bootstrap scheme for statistical inference. We demonstrate the performance of the proposed methods through simulation studies and apply these methods to estimate the efficiency gain of covariate adjustment in Covid-19 therapeutic trials.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkad007","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T08:08:11Z","timestamp":1678781291000},"page":"356-377","source":"Crossref","is-referenced-by-count":9,"title":["Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data"],"prefix":"10.1093","volume":"85","author":[{"given":"Xiudi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Washington , Seattle, WA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington , Seattle, WA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Luedtke","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Washington , Seattle, WA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"issue":"2","key":"2023050420355558200_qkad007-B1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jclinepi.2009.06.002","article-title":"A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals","volume":"63","author":"Austin","year":"2010","journal-title":"Journal of Clinical Epidemiology"},{"issue":"4","key":"2023050420355558200_qkad007-B2","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"},{"issue":"12","key":"2023050420355558200_qkad007-B3","doi-asserted-by":"crossref","first-page":"343","DOI":"10.15585\/mmwr.mm6912e2","article-title":"Severe outcomes among patients with coronavirus disease 2019 (COVID-19)\u2014United States, February 12\u2013March 16, 2020","volume":"69","author":"Bialek","year":"2020","journal-title":"Morbidity and Mortality Weekly Report"},{"key":"2023050420355558200_qkad007-B4","volume-title":"Efficient and adaptive estimation for semiparametric models","author":"Bickel","year":"1993"},{"issue":"18","key":"2023050420355558200_qkad007-B5","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1002\/sim.6507","article-title":"Leveraging prognostic baseline variables to gain precision in randomized trials","volume":"34","author":"Colantuoni","year":"2015","journal-title":"Statistics in Medicine"},{"issue":"3","key":"2023050420355558200_qkad007-B6","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s10985-018-9428-5","article-title":"Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards","volume":"25","author":"D\u00edaz","year":"2019","journal-title":"Lifetime Data Analysis"},{"issue":"2","key":"2023050420355558200_qkad007-B7","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1111\/biom.12450","article-title":"Enhanced precision in the analysis of randomized trials with ordinal outcomes","volume":"72","author":"D\u00edaz","year":"2016","journal-title":"Biometrics"},{"issue":"5","key":"2023050420355558200_qkad007-B8","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1214\/aos\/1176324307","article-title":"Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression","volume":"23","author":"Doksum","year":"1995","journal-title":"Ann Stat"},{"key":"2023050420355558200_qkad007-B9","author":"FDA","year":"2021"},{"key":"2023050420355558200_qkad007-B10","author":"Gill","year":"1997"},{"issue":"1","key":"2023050420355558200_qkad007-B11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/1745-6215-15-139","article-title":"The risks and rewards of covariate adjustment in randomized trials: An assessment of 12 outcomes from 8 studies","volume":"15","author":"Kahan","year":"2014","journal-title":"Trials"},{"issue":"2","key":"2023050420355558200_qkad007-B12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1111\/j.2517-6161.1980.tb01109.x","article-title":"Regression models for ordinal data","volume":"42","author":"McCullagh","year":"1980","journal-title":"Journal of the Royal Statistical Society. 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