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dc.contributor.authorDoshi, Peter
dc.date.accessioned2017-09-08T12:08:49Z
dc.date.available2018-09-27T12:05:35Z
dc.date.issued2017-08-22
dc.identifier.citationMayo-Wilson, E., Li, T., Fusco, N., Bertizzolo, L., Canner, J.K., Cowley, T., Doshi, P., Ehmsen, J., Gresham, G., Guo, N., Haythornthwaite, J.A., Heyward, J., Hong, H., Pham, D., Payne, J.L., Rosman, L., Stuart, E.A., Suarez-Cuervo, C., Tolbert, E., Twose, C., Vedula, S., Dickersin, K. (2017). Cherry-Picking by Trialists and Meta-Analysts Can Drive Conclusions About Intervention Efficacy. Journal of Clinical Epidemiology. DOI: 10.1016/j.jclinepi.2017.07.014
dc.identifier.urihttp://hdl.handle.net/10713/7090
dc.description.abstractObjective: To determine whether disagreements among multiple data sources affect systematic reviews of randomized clinical trials (RCTs). Study Design and Setting: Eligible RCTs examined gabapentin for neuropathic pain and quetiapine for bipolar depression, reported in public (e.g., journal articles) and non-public sources (clinical study reports [CSRs] and individual participant data [IPD]). Results: We found 21 gabapentin RCTs (74 reports, six IPD) and seven quetiapine RCTs (50 reports, one IPD); most were reported in journal articles (18/21 [86%] and 6/7 [86%], respectively). When available, CSRs contained the most trial design and risk of bias information. CSRs and IPD contained the most results. For the outcome domains “pain intensity” (gabapentin) and “depression” (quetiapine), we found single trials with 68 and 98 different meta-analyzable results, respectively; by purposefully selecting one meta-analyzable result for each RCT, we could change the overall result for pain intensity from effective (standardized mean difference [SMD]=-0.45; 95%CI -0.63 to -0.27) to ineffective (SMD=-0.06; 95%CI -0.24 to 0.12). We could change the effect for depression from a medium effect (SMD=-0.55; 95%CI -0.85 to -0.25) to a small effect (SMD=-0.26; 95%CI -0.41 to -0.1). Conclusions: Disagreements across data sources affect the effect size, statistical significance, and interpretation of trials and meta-analyses.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.subjectclinical study reportsen_US
dc.subjectdata comparisonen_US
dc.subjectpublication biasen_US
dc.subjectreporting biasen_US
dc.subject.lcshSystematic reviews (Medical research)en_US
dc.subject.meshData Accuracyen_US
dc.titleCherry-picking by trialists and meta-analysts can drive conclusions about intervention efficacyen_US
dc.typeArticleen_US
dc.typeManuscripten_US
dc.identifier.doi10.1016/j.jclinepi.2017.07.014
dc.identifier.ispublishedYesen_US
dc.description.urinameFull Texten_US
refterms.dateFOA2019-02-19T18:15:15Z
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