A0348
Title: Overcoming model uncertainty: How equivalence tests can benefit from model averaging
Authors: Niklas Hagemann - University of Cologne (Germany) [presenting]
Kathrin Moellenhoff - University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany (Germany)
Abstract: A common problem in numerous research areas, particularly in clinical trials, is to test whether the effect of an explanatory variable on an outcome variable is equivalent across different groups. In practice, these tests are frequently used to compare the effect between patient groups, e.g., based on gender, age, or treatments. Equivalence is usually assessed by testing whether the difference between the groups does not exceed a pre-specified equivalence threshold. Such tests are often based on the distance between two parametric models. These approaches have one thing in common: They are based on the assumption that the true underlying regression model is known. A flexible extension of such methodology is proposed that uses model averaging in order to overcome this assumption and make the test applicable under model uncertainty. Model averaging is introduced based on smooth AIC weights. In order to ensure numerical stability, a testing procedure is proposed which makes use of the duality between confidence intervals and hypothesis testing. The validity of the approach is demonstrated by a simulation study. A time-response case study demonstrates the practical relevance of the approach.
