All Times EDT
Keywords: Adaptive Trial Designs, Bootstrap, Platform Trials, Splines, Trends, Glioblastoma;
A common assumption of data analysis in clinical trials is that patient characteristics, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years this hypothesis may be violated. Ignoring variations of the outcome distribution of patients over time, under the control and experimental treatments, can lead to biased treatment effects estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes for control and experimental treatments with splines. The second adjustment procedure leverages conditional inference principles, which have been used to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends of the outcome distributions in Bayesian response-adaptive designs and in platform trials, and we investigate the proposed methods with simulations and in the analysis of a Glioblastoma study