In the era of modern clinical development, several companies are turning towards novel and innovative approaches such as Bayesian methods, to minimize costs, accelerate time-to-market and boost efficiency. Bayesian methods bring flexibility and speed to clinical trial design and analysis. Yet access to such designs has been limited by the need for powerful computational modelling and deep statistical expertise. In this blog, I touch upon some advantages of using Bayesian trial designs and the challenges faced in the process. Learn about a new solution that broadens and simplifies access to complex Bayesian clinical trial designs.
Advantages of Bayesian Clinical Trial Designs
Every year, sponsors hesitating to use complex innovative designs routinely miss opportunities to optimize the speed, savings and probability of success of their clinical trials. Clinical trials with advanced innovative designs use adaptive methods, Bayesian methods and other novel clinical trial design techniques to increase efficiency and reduce costs.
Bayesian methods are popular for their ability to create flexible trial designs and accelerate learning while doing so. They have played a key role in transforming clinical research in therapeutic areas such as, oncology and rare diseases, where they protect patients by efficient modeling. These methods have also been extremely effective in medical device trials, where the gains in effectiveness can be quickly calculated using Bayes’ rule.
Bayesian methods allow clinical investigators to leverage historical trial data as well as learnings from new data as it accrues throughout a trial. The result is better-informed decision making, greater program flexibility, and the ability to run smaller, more resource-efficient trials.
Cytel has a premier team of Bayesian experts who have invented and championed numerous successful early-phase clinical trial design methods including i3+3, MUCE, and more. They have prepared dozens of biotechs to sell their assets to larger pharmaceuticals between Phase 2 and Phase 3.
Challenges of Using Bayesian Methods
As trial designers turn to open-source or home-grown trial design solutions to meet the demands of modern drug development, they experience a range of challenges, from a lack of user-friendliness and quality control to difficulty accessing the computational power needed to deploy these complex methods. Publicly available solutions often rely on unverified bespoke coding, which can introduce risks as well as time investment to clinical programs.
Underpowered, on-premise deployment models limit access to innovation and cannot support the most computationally intensive designs. Additionally, many biostatisticians may not readily have access to the cloud computing power to make these design approaches practical within the time constraints afforded for statistical design.
Novel Designs Adoption Made Easy with East Bayes
East® Bayes, an enhanced web-based extension of the East® platform that enables adoption of the most computationally intensive Bayesian clinical trial designs – without the need for powerful on-site computers. It offers a suite of complex phase I dose-escalation and phase II dose-finding designs for greater speed, flexibility, and cost-effectiveness in early clinical development.
Download the new East Bayes Infographic to learn how you can collapse the multi-day process of designing these computationally intensive clinical trials into a matter of minutes.
About the Author:
Mansha Sachdev specializes in content creation and knowledge management. She holds an MBA degree and has 11 years of experience in handling various facets of marketing, across industries. At Cytel, Mansha is a Content Marketing Manager and is responsible for producing informative content that is related to the pharmaceutical and medical devices industries.
