Quality of sample size estimation in trials of medical devices: high-risk devices for neurological conditions as example

dc.contributor.authorOlberg, Britta
dc.contributor.authorPerleth, Matthias
dc.contributor.authorFelgenträger, Katja
dc.contributor.authorSchulz, Sandra
dc.contributor.authorBusse, Reinhard
dc.date.accessioned2019-02-11T17:21:03Z
dc.date.available2019-02-11T17:21:03Z
dc.date.issued2017
dc.descriptionDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.de
dc.descriptionThis publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.en
dc.description.abstractBackground: The aim of this study was to assess the quality of reporting sample size calculation and underlying design assumptions in pivotal trials of high-risk medical devices (MDs) for neurological conditions. Methods: Systematic review of research protocols for publicly registered randomized controlled trials (RCTs). In the absence of a published protocol, principal investigators were contacted for additional data. To be included, trials had to investigate a high-risk MD, registered between 2005 and 2015, with indications stroke, headache disorders, and epilepsy as case samples within central nervous system diseases. Extraction of key methodological parameters for sample size calculation was performed independently and peer-reviewed. Results: In a final sample of seventy-one eligible trials, we collected data from thirty-one trials. Eighteen protocols were obtained from the public domain or principal investigators. Data availability decreased during the extraction process, with almost all data available for stroke-related trials. Of the thirty-one trials with sample size information available, twenty-six reported a predefined calculation and underlying assumptions. Justification was given in twenty and evidence for parameter estimation in sixteen trials. Estimates were most often based on previous research, including RCTs and observational data. Observational data were predominantly represented by retrospective designs. Other references for parameter estimation indicated a lower level of evidence. Conclusions: Our systematic review of trials on high-risk MDs confirms previous research, which has documented deficiencies regarding data availability and a lack of reporting on sample size calculation. More effort is needed to ensure both relevant sources, that is, original research protocols, to be publicly available and reporting requirements to be standardizeden
dc.identifier.eissn1471-6348
dc.identifier.issn0266-4623
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/9096
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-8197
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc610 Medizin und Gesundheitde
dc.subject.othermedical devicesen
dc.subject.otherrandomized controlled trialsen
dc.subject.othersample size calculationen
dc.subject.otherneurologicalen
dc.titleQuality of sample size estimation in trials of medical devices: high-risk devices for neurological conditions as exampleen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1017/S0266462317000265
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleInternational Journal of Technology Assessment in Health Careen
dcterms.bibliographicCitation.originalpublishernameCambridge University Pressen
dcterms.bibliographicCitation.pageend110
dcterms.bibliographicCitation.pagestart103
dcterms.bibliographicCitation.volume33
tub.accessrights.dnbdomain
tub.affiliationFak. 7 Wirtschaft und Management::Inst. Technologie und Management (ITM)::FG Management im Gesundheitswesende
tub.affiliation.facultyFak. 7 Wirtschaft und Managementde
tub.affiliation.groupFG Management im Gesundheitswesende
tub.affiliation.instituteInst. Technologie und Management (ITM)de
tub.publisher.universityorinstitutionTechnische Universität Berlinde

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