In this era of personalized medicine, one should reevaluate new medicines and treatments for cancer, says Prof. Donald Berry, Ph.D.,
Dr. Berry, professor and chair of the Department of Biostatistics and Applied Mathematics at The University of Texas M. D. Anderson Cancer Center, sees a need to rethink how clinical trials are designed and conducted in the U.S. He thinks the current 50-year-old system, will not be able to cope with the strain of 21st century medicine, and something should be done to relieve that strain.
Berry outlines his new approach to clinical trials in the January 2006 issue of Nature Reviews Drug Discovery. In the article, he advocates that the statistical method used to evaluate new drugs be turned on its head. He says the statistical method used nearly exclusively to design and monitor clinical trials today—a method called frequentist or Neyman-Pearson (for the statisticians who advocated its use)—is so narrowly focused and rigorous in its requirements that it limits innovation and learning.
The solution, which he has advocated for over 30 years, is to adopt a system called the Bayesian method, a statistical approach he says is more in line with how science works. He sites examples of Bayesian approaches being used routinely in physics, geology and other sciences. And he is putting his approach to the test at M. D. Anderson, where over 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 is how each method deals with uncertainty, an inescapable component of any clinical trial. Unlike frequentist methods, Bayesian methods assign anything unknown a probability using information from previous experiments, explains Berry. In other words, 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 possible to do continuous updating as information accrues. This characteristic makes it possible for medicine to build adaptive designs in clinical trials.
Berry 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.
Doctors want to be able to design trials to look at multiple potential treatment combinations and use biomarkers to determine who is responding to which medication. At the end of the day, when they enroll the last patient in the study, they want to be able to treat that patient optimally depending on the patient's disease characteristics. In the Bayesian approach, the trial design exploits the results as the trial is ongoing and adapts itself based on these interim results. This kind of approach is anathema in the standard approach.
However, Berry argues, such flexibility is crucial to clinical trials in the 21st century.