A Bayesian approach to clinical trial designs in dermatology with multiple simultaneous treatments per subject and multiple raters

• Verfasst von: Uhlmann, Lorenz [VerfasserIn]
• Verfasst von: Stock, Christian [VerfasserIn]
• Verfasst von: Vandemeulebroecke, Marc [VerfasserIn]
• Verfasst von: Müller-Christmann, Christine [VerfasserIn]
• Verfasst von: Kieser, Meinhard [VerfasserIn]
• Titel: A Bayesian approach to clinical trial designs in dermatology with multiple simultaneous treatments per subject and multiple raters
• Verf.angabe: Lorenz Uhlmann, Christian Stock, Marc Vandemeulebroecke, Christine Mueller-Christmann, Meinhard Kieser
• E-Jahr: 2023
• Jahr: August 2023
• Umfang: 8 S.
• Fussnoten: Gesehen am 23.01.2024
• Titel Quelle: Enthalten in: Contemporary clinical trials
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 2005
• Jahr Quelle: 2023
• Band/Heft Quelle: 131(2023), Artikel-ID 107233, Seite 1-8
• ISSN Quelle: 1559-2030
• DOI: doi:10.1016/j.cct.2023.107233
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Bayesian method
• Sach-SW: Clinical trial
• Sach-SW: Dermatology
• Sach-SW: Pairwise comparisons
• K10plus-PPN: 187872195X

Abstract

We consider the statistical analysis of clinical trial designs with multiple simultaneous treatments per subject and multiple raters. The work is motivated by a clinical research project in dermatology where different hair removal techniques were assessed based on a within-subject comparison. We assume that clinical outcomes are assessed by multiple raters as continuous or categorical scores, e.g. based on images, comparing two treatments on the subject-level in a pairwise manner. In this setting, a network of evidence on relative treatment effects is generated, which bears strong similarities to the data underlying a network meta-analysis of clinical trials. We therefore build on established methodology for complex evidence synthesis and propose a Bayesian approach to estimate relative treatment effects and to rank the treatments. The approach is, in principle, applicable to situations with any number of treatment arms and/or raters. As a major advantage, all available data is brought into a network and analyzed in one single model, which ensures consistent results among the treatment comparisons. We obtain operating characteristics via simulation and illustrate the method with a real clinical trial example.