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dc.contributor.authorShahn, Zach
dc.contributor.authorSpear, Phoebe
dc.contributor.authorLu, Helen
dc.contributor.authorJiang, Sharon
dc.contributor.authorZhang, Suki
dc.contributor.authorDeshmukh, Neil
dc.contributor.authorXu, Shenbo
dc.contributor.authorNg, Kenney
dc.contributor.authorWelsch, Roy
dc.contributor.authorFinkelstein, Stan
dc.date.accessioned2022-10-06T14:28:17Z
dc.date.available2022-10-06T14:28:17Z
dc.date.issued2022-09
dc.identifier.urihttps://hdl.handle.net/1721.1/145705
dc.description.abstractWith availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.en_US
dc.language.isoen
dc.publisherWileyen_US
dc.relation.isversionof10.1002/pds.5491en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceWileyen_US
dc.titleSystematically exploring repurposing effects of antihypertensivesen_US
dc.typeArticleen_US
dc.identifier.citationShahn, Zach, Spear, Phoebe, Lu, Helen, Jiang, Sharon, Zhang, Suki et al. 2022. "Systematically exploring repurposing effects of antihypertensives." Pharmacoepidemiology and Drug Safety, 31 (9).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMIT-IBM Watson AI Laben_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.relation.journalPharmacoepidemiology and Drug Safetyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-10-06T14:00:54Z
dspace.orderedauthorsShahn, Z; Spear, P; Lu, H; Jiang, S; Zhang, S; Deshmukh, N; Xu, S; Ng, K; Welsch, R; Finkelstein, Sen_US
dspace.date.submission2022-10-06T14:00:56Z
mit.journal.volume31en_US
mit.journal.issue9en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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