Bayesian methods have been playing a key role in transforming clinical research in therapeutic areas such as oncology and rare diseases, and in addressing clinical development challenges for COVID-19 drugs, devices and biologics. From early-phase trials to late-phase development, utilizing Bayesian tools can expedite and/or de-risk trials, even when used to augment a Frequentist framework. Yet access to such designs has been limited by the need for powerful computational modelling and deep statistical expertise.
Bayesian research scientists, Dr. Ofir Harari, Dr. Pantelis Vlachos and Dr. Yannis Jemiai, discuss the advantages of using Bayesian methods in early- and late-phase clinical trial design, in their latest publication.
Bayesian approaches provide a variety of new opportunities for efficient and flexible clinical trials. The Bayesian approach allows every new piece of data to serve as evidence to update a hypothesis. Bayes’ rule consists of a “prior” either based on evidence already collected, or scientific findings in the case of early-phase trials. A rule then explains how to update these priors in order to make sense of newly collected evidence. A “posterior” is then the result of the prior being updated in light of this new evidence. As the trials evolves with new in-trial insights, these Bayesian methods enable statisticians and sponsors to create flexible trial designs and accelerate learning.
Frequentist designs can often require higher sample sizes than Bayesian methods, and are considered by some to be less flexible and less intuitive. However, the decision to use Frequentist or Bayesian designs is a matter of context. After considering all the parameters, it is on the statistician’s expertise to choose the method that is best suited to the objectives of the trial.
When taking a closer look at the context, scientists at Cytel have often advocated for mixed or hybrid methods to be used for a single submission. Not every sponsor realizes, (and it is important to note,) that Bayesian methods can also be used to strengthen insights of trials that are Frequentist. Bayesian tools can be used to prepare or augment trials that make use of Frequentist designs. Suppose a Frequentist trial faces limited resources or needs a little more power to convince regulators of its statistical rigor. Bayesian methods can be used to borrow from historical datasets, incorporating already existing data into a new clinical trial to augment new findings.
Bayesian designs are frequently used in early-phase trials due to their flexibility and efficiency. As very little is known about the response of a drug or its toxicity, patients and sponsors benefit from a trial design that enables frequent interim looks. Bayesian methods provide the opportunities for adjusting doses and stopping for futility when required. They offer an intuitive approach to clinical development, maximizing the use of available information at each interim analysis. For fast recruiting trials, Bayesian designs offer high confidence in early futility, efficacy and sample size decision making, basing the decision on the consistency of the results from two or more early interim analyses.
Bayesian methods can also be integrated with Frequentist clinical trial designs to obtain clearer Benefit-Risk profiles for a number of new therapies. Ethically, every ounce of data collected ought to be used for these calculations, and Bayesian methods allow this to occur.
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About the Author of Blog:
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.
