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dc.contributor.authorSuls, JM
dc.contributor.authorAlfano, C
dc.contributor.authorYap, C
dc.date.accessioned2022-12-07T13:29:13Z
dc.date.available2022-12-07T13:29:13Z
dc.date.issued2022-09-08
dc.identifier.citationHarvard Data Science Review, 2022,
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5596
dc.identifier.doi10.1162/99608f92.d99e6ff5
dc.description.abstractTreatment of patients who suffer from concurrent health conditions is not well served by (1) evidence-based clinical guidelines that mainly specify treatment of single conditions and (2) conventional randomized controlled trials (RCTs) that identify treatments as safe and effective on average. Clinical decision-making based on the average patient effect may be inappropriate for treatment of those with multimorbidity who experience burdens and obstacles that may be unique to their personal situation. We describe how the personalized (N-of-1) trials can be integrated with an automatic platform and virtual/remote technologies to improve patient-centered care for those living with multimorbidity. To illustrate, we present a hypothetical clinical scenario-survivors of both coronavirus disease 2019 (COVID-19) and cancer who chronically suffer from sleeplessness and fatigue. Then, we will describe how the four standard phases of conventional RCT development can be modified for personalized trials and applied to the multimorbidity clinical scenario, outline how personalized trials can be adapted and extended to compare the benefits of personalized trials versus between-subject trial design, and explain how personalized trials can address special problems associated with multimorbidity for which conventional trials are poorly suited.
dc.languageen
dc.language.isoeng
dc.publisherMIT Press
dc.relation.ispartofHarvard Data Science Review
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePersonalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity.
dc.typeJournal Article
dcterms.dateAccepted2022-03-06
dc.date.updated2022-12-07T13:28:47Z
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1162/99608f92.d99e6ff5
rioxxterms.licenseref.startdate2022-09-08
rioxxterms.typeJournal Article/Review
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.publication-statusPublished online
pubs.publisher-urlhttp://dx.doi.org/10.1162/99608f92.d99e6ff5
icr.researchteamClin Trials & Stats Unit
dc.contributor.icrauthorYap, Christina
icr.provenanceDeposited by Mr Arek Surman on 2022-12-07. Deposit type is initial. No. of files: 1. Files: ynjjhteucyqmmed02g30fc0cz536mcuc.pdf


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/