The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
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.
Platform trials are a new type of clinical trials where multiple interventions can be evaluated simultaneously against a common control group within a single master protocol. Platform trial designs are an extension of adaptive trial designs that are sometimes referred to as a multi-arm, multi-stage (MAMS) design, as multiple interventions (‘‘multi-arm’’) undergoing multiple interim evaluations (‘‘multi-stage’’) are part of the design features.
This autumn Cytel has been holding a number of webinars on Platform Trials, ranging from a discussion with Cyrus Mehta on statistical innovations to incentivize more sponsors to consider platform trials, to next week's event with Jason Connor (Confluence) on the use of Bayesian methods for these innovative trial designs.
In a recent webinar Jay Park, Director of Trials Research for Cytel in Canada, presented a webinar to review the concept of platform trials and discuss important design considerations for platform trials. Jay is the author of several leading papers on Platform Trials, including one in CA: A Cancer Journal for Clinicians, the journal with the world's highest impact factor. He has also produced a complimentary primer on the subject which you can download here.
Continue reading this blog to get a summary of his talk. Click the button to access the on demand webinar.
The Cytel YouTube Channel hosts a wealth of video presentations from Cytel experts as well as external industry and academic speakers about various aspects of clinical trial designs and their implementation. In this blog, we've gathered 6 popular resources from the channel on topics from quantitative decision-making through to overcoming challenges in management of oncology trials. Read on to learn more and access the videos.
With an increasing interest in platform designs and other innovative designs that involve multiple comparisons over multiple stages, the importance of Multi-Arm Multi-Stage ( MAMS) designs is set to rise.
A paper "Best practices case studies for 'less well-understood' Adaptive designs", has been published by the DIA Scientific Working Group on Adaptive Designs as a twin document to the previously discussed "Challenges and Opportunities of 'Less Well Understood' Adaptive Designs". This publication furthers understanding by reviewing 10 important case studies and sharing details on their design and operational characteristics, as well as related regulatory interactions.
To read an abstract and details of the full publication click here.
In this blog we'll take a look at some of the case studies under discussion.
Last week, we were delighted to announce the release of East 6.4 bringing further cutting –edge approaches to the East user community. East is the industry standard platform for clinical trial design, simulation, and monitoring, improving scientific productivity during the critical planning stages of clinical development. In this blog we catch up with Yannis Jemiai, VP of Cytel to gain some behind-the-scenes insights into the development and new features of this important release.
There has been increasing interest in multi-arm multi-stage trials with treatment selection and sample size re-estimation at interim analysis. The East 6.4 release incorporates new Multi-Arm Multi-Stage (MAMS) module to support statisticians designing these studies. In this blog, we take a high level look at some of the features and advantages of this approach.
We have often said that one of the greatest benefits of an adaptive clinical trial is the flexibility it affords for decision-making . Often this flexibility is taken to mean that sponsors have some leeway to accommodate events - foreseen or unforeseen, challenging or advantageous - midway through a trial. However, when designing a trial, it is often possible to leverage this flexibility towards the sponsor's advantage.
Recently, a client approached us, determined to select the best of eight candidate doses to move forward into a pivotal Phase 3 trial. Like many in their situation, our client worried that testing all eight doses would require unnecessary time and expense. However, given a variety of other constraints, a subpar Phase 3 dose was simply not an option.
Most of us are primed to think about the design of adaptive clinical trials as a narrow set of techniques applied to a specific set of problems. If you’re worried about the power of your study, for example, you can turn to your toolkit of adaptive methods and find a suitable use for sample size re-estimation. If the concern is getting the best possible dose, a multi-arm study which drops doses after an interim look seems like the perfect solution.
Clinical development teams often look to adaptive designs, only after identifying specific objectives that a conventional trial may struggle to resolve. However, this approach has its limitations. An adaptive strategy might improve a trial design, even when a conventional strategy supplies a reasonable, (though less efficient,) alternative.