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J Thorac Cardiovasc Surg 2006;132:249-251
© 2006 The American Association for Thoracic Surgery

Statistics for the Rest of Us

Believability of clinical trials: A diagnostic testing perspective

Departments of Medicine and Epidemiology and Biostatistics, Lerner Medical College of Case Western Reserve University, and the Department of Cardiovascular Medicine, The Cleveland Clinic Foundation, Cleveland, Ohio

Received for publication November 21, 2005; accepted for publication March 3, 2006.

^_* Address for reprints: Michael S. Lauer, MD, FACC, FAHA, The Cleveland Clinic Foundation, 9500 Euclid Ave/Desk JJ40, Cleveland, OH 44122 (Email: Lauerm@ccf.org).

The first 20% of the full text of this article appears below.

Clinical trials are the medical community's diagnostic tests. When a physician sends a patient for a conventional diagnostic test, he or she is asking whether a certain disease is likely to be present. Similarly, when the medical community designs and executes a clinical trial, it wants to know whether a certain treatment is likely to work. And just like a diagnostic test, a clinical trial has the potential for yielding an incorrect, erroneous result.

The traditional approach to reporting probability of error in clinical trials includes P values and statements of power. The P value is the probability that a positive trial result is just the result of chance, given an assumed truth of the null hypothesis, which is that the treatment does not work. Power refers to the ability of a trial to show that a treatment works, given that in truth it actually does. Although presenting P values and statements of power has become lore in reporting clinical trials, careful reflection shows that clinicians might not gain as much value from them as they might think.

When a clinician reads a diagnostic test report that is "positive," the next question is, "How likely is it that my patient has the disease?" That is, what is the likelihood that this positive report is in fact correct? As described by Bayesian theory, the likelihood of a correct result given a positive test result, or positive predictive value, is related not only to the sensitivity and specificity . . . [Full Text of this Article]

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