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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
Abstract #313160
Title: Inference in response-adaptive clinical trials when the enrolled population varies over time
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

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.

Authors who are presenting talks have a * after their name.

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