New generation clinical trials could save time and money, improve patient care

As we enter the era of personalized medicine, it is time to take a fresh look at how we evaluate new medicines and treatments for cancer, according to 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.

"We need to rethink how we design and conduct clinical trials in the United States," says Berry. "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."

Berry outlines his approach to conducting clinical trials in the January 2006 issue of Nature Reviews Drug Discovery. In the 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, 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.

His solution, which he has advocated for more than 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 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. Unlike frequentist methods, explains Berry, Bayesian methods assign anything unknown a probability using information from previous experiments. In other words, Bayesian method

Contact: Nancy Jensen
University of Texas M. D. Anderson Cancer Center

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