Conducting a transparent, targeted and robust benefit-risk assessment of a new drug or product is one of the most complex tasks and yet, very important. There are many frameworks available to do the assessment in a qualitative way. However, several quantitative methods have been proposed in the recent years to ensure more transparency and consistency in how decisions are made across different regulatory and payer organizations.
Bayesian methodologies for one, have proven to be very effective for quantitative benefit-risk assessment. They can also be leveraged throughout the life cycle of a medical product to support and augment clinical judgment and qualitative benefit-risk assessments.
Benefit-risk assessment is crucial to determine whether a drug is effective and that its expected benefits outweigh the potential risks to patients. It is the foundation for FDA’s regulatory review of human drugs and biologics. The 21st Century Cures Act requires the Agency to issue guidance describing how FDA anticipates incorporating relevant patient experience data and related information into the structured benefit-risk assessment framework to inform regulatory decision-making. 1
Communicating the trade-off of benefits and risks in a clear and transparent manner, using all available evidence, is critical to ensure that the best decisions are made. Bayesian inference, with its coherent approach for integrating different sources of information and uncertainty, along with its links to optimal decision theory, provides a natural framework to perform quantitative assessments of the benefit-risk trade-off.
There are many advantages of using a Bayesian statistical framework for benefit-risk assessment. It supports decision making as it allows you to weigh different factors according to different prior beliefs that an individual stakeholder may have. For example, the factors a regulator would consider as opposed to a payer or a patient can vary. These differences can be incorporated into a quantitative assessment of benefit-risk much more easily using a Bayesian framework.
In the recent times, more drugs have complex benefit-risk profiles. Traditional study designs and statistical methods are limited in terms of how you adequately assess the benefit-risk of an experimental intervention. Therefore, there is an increasing need for innovation.
During the calculation of benefit-risk profiles, the ethical standard should take into consideration every piece of data available at our disposal, from historical data from other trials to the effects of the current therapy on various endpoints. Bayesian methods enable trial sponsors to collate these various forms of data into one benefit-risk calculation. These methods can also be integrated with Frequentist clinical trial designs to obtain clearer benefit-risk profiles for a number of new therapies.
Bayesian research scientists, Dr. Ofir Harari, Dr. Pantelis Vlachos and Dr. Yannis Jemiai, offer an in-depth look at how Bayesian methods are set to transform the future of the clinical research industry, in their latest publication.
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.