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dc.contributor.authorYe, Ting
dc.contributor.authorShao, Jun
dc.contributor.authorYi, Yanyao
dc.contributor.authorZhao, Qingyuan
dc.date.accessioned2022-03-08T00:30:36Z
dc.date.available2022-03-08T00:30:36Z
dc.date.issued2022
dc.identifier.issn0162-1459
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334744
dc.description.abstractIn randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (1) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (2) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (3) robust standard error: variance estimation should be robust to model misspecification and heteroscedasticity. To achieve these, we recommend a model-assisted estimator under an analysis of heterogeneous covariance working model including all covariates utilized in randomization. Our conclusions are based on an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and regression adjustment alter statistical efficiency. Our theory is more general than the existing ones in terms of studying arbitrary functions of response means (including linear contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain, optimality, and universal applicability.
dc.description.sponsorshipIsaact Newton Trust
dc.publisherInforma UK Limited
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectstat.ME
dc.subjectstat.ME
dc.titleToward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
dc.typeArticle
dc.publisher.departmentDepartment of Pure Mathematics And Mathematical Statistics
dc.date.updated2022-03-05T21:27:54Z
prism.publicationNameJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
dc.identifier.doi10.17863/CAM.82174
dcterms.dateAccepted2022-02-21
rioxxterms.versionofrecord10.1080/01621459.2022.2049278
rioxxterms.versionAM
dc.contributor.orcidZhao, Qingyuan [0000-0001-9902-2768]
dc.identifier.eissn1537-274X
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-03-27
cam.orpheus.successMon Apr 25 18:24:13 BST 2022 - Embargo updated
cam.orpheus.counter3
cam.depositDate2022-03-05
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-03-26


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International