Thursday, November 16, 2017z
3:30 – 4:30 PM
Kresge 202A
Contact Valerie Coffee to schedule a meeting
Joe Koopmeiners
Associate Professor, Division of Biostatistics
University of Minnesota
A Multi-source Adaptive Platform Design for Emerging Infectious Diseases
Emerging infectious diseases challenge traditional paradigms for clinical translation of therapeutic interventions. The Ebola virus disease outbreak in West Africa was a recent example which heralded the need for alternative designs that can be sufficiently flexible to compare multiple potentially effective treatment regimes in a context with high mortality and limited available treatment options. The PREVAIL II master protocol was designed to address these concerns by sequentially evaluating multiple treatments in a single trial and incorporating aggressive interim monitoring with the purpose of identifying efficacious treatments as soon as possible. One shortcoming, however, is that supplemental information from controls in previous trial segments was not utilized. In this talk we address this limitation by proposing an adaptive design methodology that facilitates “information sharing” across potentially non-exchangeable segments using multi-source exchangeability models (MEMs). The design uses multi-source adaptive randomization to target information balance within a trial segment in relation to posterior effective sample size. When compared to the standard platform design, we demonstrate that MEMs with adaptive randomization can improve power with limited type-I error inflation. Further, the adaptive platform effectuates more balance with respect to the distribution of acquired information among study arms with more patients randomized to experimental regimens which, when effective, yields reductions in the overall mortality rate for trial participants.
