Bayesian Methods for Multiple Cohort Expansion (MuCE) designs

November 16, 2020

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MUCE is a Bayesian solution for cohort expansion trials where multiple dose(s) and multiple indication(s) are tested in parallel. Such methods are particularly important for areas like oncology where several doses and several indications must be tested for successful completion of early phase trials, and optimal choice of dose and population to move on from early phase to a reasonable dosage for Phase 3.

Note that for these situations the number of comparator arms for a trial can increase rather rapidly. Testing three doses with three indications essentially requires 9 different trials. An efficient way to test a higher number of trials is therefore necessary for accelerated clinical development.

Built on Bayesian hierarchical models with multiplicity control, MUCE adaptively borrows information across patient groups from different indications treated with different doses.

The hierarchical model reflects the fact that treatment-arms with similar doses or for similar indications might be more similar than others, giving appropriate weight to such borrowing. This enables MUCE to Control Type 1 Error while increasing power and reducing sample size.
These efficient designs can be applied in any clinical trials with two or more arms. For an expansion cohort trial in the US, the MUCE design showed saving in sample size of up to 16.67% compared to Simon’s 2-stage design.

A particular application of MUCE is in Phase 1b expansion cohort trials. In these trials, one or more candidate dose levels with reasonable safety profiles are selected for further evaluation of efficacy following a dose escalation part (Phase 1a) in which different dose levels of the drug are investigated for safety. This leads to the determination of the maximum tolerated dose (MTD). Following that, up to three doses typically, none higher than the MTD, are considered for expansion in the phase 1b study, and a number of different indications, say I,-indications based on histology are considered. This leads to a maximum of 3*I arms, each with a unique dose-indication combination.

About U-Design

U-Design was developed by Laiya Consulting as a clinical trial biostatistics SaaS package. Offering a number of innovative methods, users can utilize the tools and functions provided on U-Design to create their own customized clinical trials.

Watch a Cytel webinar on U-Design version 1.4, presented by Dr. Yuan Ji, a consultant for Cytel and the founder of Laiya Consulting.

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About Pantelis Vlachos

Pantelis Vlachos photo on black 2018Pantelis is Principal/Strategic Consultant for Cytel, Inc. based in Geneva. He joined the company in January 2013. Before that, he was a Principal Biostatistician at Merck Serono as well as a Professor of Statistics at Carnegie Mellon University for 12 years. His research interests lie in the area of adaptive designs, mainly from a Bayesian perspective, as well as hierarchical model testing and checking although his secret passion is Text Mining. He has served as Managing Editor of the journal “Bayesian Analysis” as well as editorial boards of several other journals and online statistical data and software archives.