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

• Activity Number: 272 - Statistical Innovations in Regulatory Science
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Biometrics Section
• Author(s): Massimiliano Russo* and Lorenzo Trippa and Steffen Ventz and Victoria Wang
• Companies: Harvard Medical School and Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health
• Keywords: Adaptive Trial Design; Bootstrap; Platform Trials; Splines; Population Trends; Glioblastoma

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 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 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.