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 enroll 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 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 in platform trials, and we investigate the proposed methods with simulations and in the analysis of a glioblastoma study.

Inference in response-adaptive clinical trials when the enrolled population varies over time

Regulatory Science Forum

A global, member-driven non-profit organization mobilizing life sciences and healthcare. DIA fosters innovation to improve health and well-being worldwide. Professionals from 80 countries continue to engage with DIA through our unparalleled membership network, educational offerings, and professional development opportunities.