"We need to rethink how we design and conduct clinical trials in the United States," says Donald Berry, Ph.D., professor and chair of the Department of Biostatistics and Applied Mathematics at The University of Texas M. D. Anderson Cancer Center. "Our current system has served us well for the past 50 years, but the demands of 21st century medicine are beginning to put a strain on the current system, and we believe we have something to relieve that strain."
In Berry’s article, he advocates turning the statistical method used to evaluate new drugs on its head. He states that the statistical method used nearly exclusively to design and monitor clinical trials today is so narrowly focused and rigorous in its requirements that it limits innovation and learning.
His solution is to adopt a system called the Bayesian method, a statistical approach he says is more in line with how science works. He is putting his approach to the test at M. D. Anderson, where more than 100 cancer-related phase I and II clinical trials are being planned or carried out using the Bayesian approach.
The main difference between the Bayesian approach and the frequentist approach to clinical trials has to do with how each method deals with uncertainty, an inescapable component of any clinical trial. Bayesian methods make use of the results of previous experiments, whereas frequentist approaches assume we have no prior results.
"Using the Bayesian approach, it is natural to do continuous updating as information accrues," says Berry. "This characteristic makes it possible for us to build adaptive designs in clinical trials." He argues that the Bayesian approach is better for doctors, patients who participate in clinical trials and for patients who are waiting for new treatments to become available.
MEDICA.de; Source: University of Texas M. D. Anderson Cancer Center