Title: Inference in response-adaptive clinical trials when the enrolled population varies over time
Authors: Massimiliano Russo - Harvard Medical School (United States) [presenting]
Steffen Ventz - Dana Farber Cancer Institute (United States)
Victoria Wang - Dana Farber Cancer Institute (United States)
Lorenzo Trippa - Dana Farber Cancer Institute (United States)
Abstract: A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enrol patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments can lead to biased treatment effect 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 with splines. The second one leverages conditional inference principles, which have been introduced 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 in the outcome distributions in Bayesian response-adaptive designs and platform trials, and we investigate the proposed methods with simulations and in the analysis of a glioblastoma study.