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Activity Number: 32 - Statistical Methods in Dose-Finding Studies
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #304276 Presentation
Title: Bayesian Hierarchical EMAX Model for Dose-Response in Early Phase Efficacy Clinical Trials
Author(s): Byron Gajewski* and Caitlyn Meinzer and Scott Berry and Gayland L Rockswold and William G Barsan and Frederick K Korley and Renee' H Martin
Companies: University of Kansas Medical Center, The University of Kansas Cancer and Medical University of South Carolina and Berry Consultants and Hennepin County Medical Center and University of Michigan and University of Michigan and Medical University of South Carolina
Keywords: Dosing design; Bayesian models; hierarchical models; EMAX; logistic

A primary goal of a phase II dose-ranging trial is to identify a correct dose before moving forward to a phase III confirmatory trial. A popular model in phase II is an independent model that puts no structure on the dose-response relationship. Unfortunately, the independent model does not efficiently use information from related doses. One very successful alternate model improves power using a pre-specified dose-response structure. Past research indicates that EMAX models are broadly successful and therefore attractive for designing dose-response trials. However, there may be instances of slight risk of non-monotone trends that need to be addressed when planning a clinical trial design. We propose to add hierarchical parameters to the EMAX model. The added layer allows information about the treatment effect in one dose to be ‘borrowed’ when estimating the treatment effect in another dose. This is referred to as the hierarchical EMAX model. Our talk compares three different models and two different design strategies. The first design considered has a fixed schedule for randomization. The second design is response adaptive randomization.

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

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