B0682
Title: Testing for similarity of multivariate mixed outcomes with application to efficacy-toxicity responses
Authors: Niklas Hagemann - University of Cologne (Germany) [presenting]
Giampiero Marra - University College London (United Kingdom)
Frank Bretz - Novartis Pharma AG (Switzerland)
Kathrin Moellenhoff - University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany (Germany)
Abstract: A common problem in clinical trials is to test whether the effect of an explanatory variable on the response, e.g. the effect of the dose of a compound on efficacy, is similar between the two groups. In this context, similarity is equivalence up to a pre-specified threshold specifying the accepted deviation between the groups. Such a question is usually assessed by testing whether the marginal effects of the explanatory variable on the response are similar, based on, for example, confidence intervals for differences or, to mention another example, the distance between two parametric models. These approaches typically assume a univariate continuous or binary outcome variable. An approach for associated bivariate binary response variables, based on the Gumbel model, has been recently introduced. A flexible extension of such methodology is proposed that builds on a generalized joint regression framework with a Gaussian copula. Compared to existing approaches, this allows for various scales of the outcome variables (e.g. continuous, binary, categorical, ordinal), including mixed outcomes as well as responses with more than two dimensions. The validity of the approach is demonstrated by means of a simulation study. An efficacy-toxicity case study demonstrates the practical relevance of the approach.
