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Comment on "Bayesian Classification of Clinical Practice Guidelines"

Steven N. Goodman, MD, PhD

Arch Intern Med. 2009;169(15):1436-1437.

	Since this article does not have an abstract, we have provided the first 150 words of the full text and any section headings.

It is hard to believe that a quarter century has passed since Diamond and Forrester started beating the Bayesian drum in their influential article titled "Clinical Trials and Statistical Verdicts: Probable Grounds for Appeal."¹ The difference is that there is now a virtual orchestra playing along, and the audience has considerably enlarged. A significant sign of progress is the US Food and Drug Administration's (FDA’s) draft guidance document on Bayesian methods for device applications that shows how the Bayesian approach can serve the needs of a regulatory agency concerned with holding bias and self-interest at bay.²

Diamond and Kaul, in their perspective, urge guideline writers to use Bayesian methods to quantify and classify the uncertainty underlying the evidence-based guidelines. There is both more and less than meets the eye in this proposal. In some important ways, the Bayesian aspect is a sideshow. To see what . . . [Full Text of this Article]

AUTHOR INFORMATION
Author Affiliations: Division of Cardiology (Drs Diamond and Kaul) and the Cedars-Sinai Heart Institute (Dr Kaul), Cedars-Sinai Medical Center, and the David Geffen School of Medicine, University of California, Los Angeles (Dr Diamond), Los Angeles.

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Bayesian Classification of Clinical Practice Guidelines
George A. Diamond and Sanjay Kaul
Arch Intern Med. 2009;169(15):1431-1435.
ABSTRACT | FULL TEXT