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dc.rights.licenseopenen_US
dc.contributor.authorAGNIEL, Denis
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIEBAUT, Rodolphe
dc.contributor.authorPARAST, Layla
dc.date.accessioned2022-10-14T10:31:16Z
dc.date.available2022-10-14T10:31:16Z
dc.date.issued2022-07-06
dc.identifier.issn1468-4357 (Electronic) 1465-4644 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/167018
dc.description.abstractEnWhen evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.
dc.language.isoENen_US
dc.title.enDoubly robust evaluation of high-dimensional surrogate markers
dc.typeArticle de revueen_US
dc.identifier.doi10.1093/biostatistics/kxac020en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed35791753en_US
bordeaux.journalBiostatisticsen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03100499
hal.version1
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Biostatistics&rft.date=2022-07-06&rft.eissn=1468-4357%20(Electronic)%201465-4644%20(Linking)&rft.issn=1468-4357%20(Electronic)%201465-4644%20(Linking)&rft.au=AGNIEL,%20Denis&HEJBLUM,%20Boris&THIEBAUT,%20Rodolphe&PARAST,%20Layla&rft.genre=article


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