A Bayesian approach to clinical trial designs in dermatology with multiple simultaneous treatments per subject and multiple raters - Uhlmann, Lorenz; Stock, Christian; Vandemeulebroecke, Marc; Müller-Christmann, Christine; Kieser, Meinhard (Contemporary clinical trials, 2023)
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.