Featured Articles
-
Hybrid Bayesian And Frequentist Clinical Trial Designs
3/25/2021
Most people know that clinical drug discovery is usually conducted using either Frequentist or Bayesian methods. These two statistical paradigms have enjoyed a degree of competition historically, with some statisticians tauting the statistical rigor of Frequentist designs and others the intuitiveness and flexibility of Bayesian clinical trials. Recently, though, a number of hybrid methods have arisen, that leverage the benefits of both paradigms for singularly powerful clinical trials. There are benefits of these combined methods.
-
Comparisons of Bayesian And Traditional Phase 2 Methods For Oncology
3/5/2021
The rise of Bayesian methods has meant that predictive power can be used to assess efficacy during these single arm Phase 2 studies, but how do they differ from traditional designs and when should they be used?
-
Simulation Based Clinical Trial Optimization
3/1/2021
The past decade has witnessed the rise of simulations-based clinical trial optimization in a manner unimaginable to most only a few years ago. Such optimization has become an integral aspect of strategic clinical trial design. Nowadays, technology can produce innumerable simulations within a short space of time. Why is it then, that some trial sponsors still struggle to make use of such simulations?
-
Selecting Your Next Clinical Trial Design Using Quantitative Decision Methods
3/1/2021
C-Suite and R&D decision-makers are always striving to make evidence-driven decisions. Yet the rules by which evidence is evaluated can bias these decisions, even when the method of decision-making seems objective. Our Chief Scientific Officer, Dr. Yannis Jemiai, has worked extensively on how to operationalize decision theoretic tools for clinical development decision-making. Here he introduces three quantitative frameworks that life-sciences decision-makers can quickly incorporate into their selection process when selecting an optimal design for their next clinical trial.
-
Easily Create Complex Bayesian Studies
2/26/2021
The number of simulation and modeling tools necessary to perform Bayesian computations requires statisticians to be well-resourced technologically. 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.
-
Bayesian Methods For Evidence Synthesis
2/18/2021
In the last few years, there has been a growing interest in historical borrowing or augmented trials. There is an increasing level of comfort in using these methodologies even in confirmatory trials setting. The key challenge in borrowing external information is the selection of appropriate historical studies or external data sources. There are benefits to historical borrowing but also potential risks (for example, Type I error and power can be impacted by the drift). This blog aims to introduce the concepts of evidence synthesis and Bayesian dynamic borrowing.
-
Computation And Clinical Trial Design: New Directions
2/1/2021
Recent advances in computational tools have made the construction of high-efficiency clinical trials more rigorous than ever, with the ability to thoroughly explore a design space consisting of hundreds of thousands of possible simulations. Not only does such capability enable the optimization and de-risking of clinical trial design, it enables sponsors to ask questions they might never have had opportunity to explore before now.
-
Bayesian Statistics In Early Phase Clinical Trials
1/7/2021
Bayesian clinical trials are gaining prominence across the clinical development journey. Learn how to employ Bayesian statistics in early phase trials with Professor Yuan Ji of the University of Chicago, including two new innovative clinical trial designs invented by Professor Ji that are quickly gaining popularity.
-
Bayesian Clinical Trial Methods For Multiple Cohort Expansion: An Innovative Clinical Trial Design
1/7/2021
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.
-
Bayesian Statistics And Health Economic Outcomes: An Interview With Dr. Bart Heeg
1/7/2021
In this two-part blog series, we interview Bart Heeg, Vice President HEOR and Founder at Ingress Health (A Cytel company). Bart provides us insights on the trends in HEOR and explains why Bayesian methods are also important for Health Economics.