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Essentials of Randomized Clinical Trials

February 2001 PACE, Vol. 24 Introduction Today, randomized clinical trials are the standard by which promising medical therapies are evaluated. This was not the case historically, when medical therapies such as the application of boiling oil to battlefield injuries were adopted on the basis of subjective belief. More recent examples of misguided beliefs include use of thalidomide during pregnancy and the use of Class I antiarrhythmics following myocardial infarction. Clinical trials provide a level of evidence qualitatively superior to other methods and are the key means of objectively evaluating new therapies. The purpose of this article is to outline the rationale for, and conduct of, a definitive clinical trial. We proceed chronologically and cover selection of the question, protocol development, monitoring of the ongoing trial, and analysis of the completed trial. Special emphasis is placed on explaining procedures that may be counterintuitive. This article discusses clinical trials with two “arms” or treatments to which patients are randomly assigned. For simplicity, one is called treatment and the other is called control. The trial is undertaken with the expectation that treatment will be shown superior to the control. The control arm could be a placebo or a standard therapy, while the treatment arm is probably a newer therapy. The focus is on a setting where a body of evidence, from theoretical or biological arguments, anecdotes, case studies, nonrandomized evaluations, and perhaps small trials, has developed so that a clear answer about the usefulness of the treatment is required. Such trials are termed phase III or therapeutic confirmatory by the Food and Drug Administration. The goal of a phase III randomized clinical trial is to answer definitively an important clinical question. The definitiveness is achieved by the elimination of bias as a potential cause for the results of the trial. Here, bias means giving treatment or control an unfair advantage, whether known or unknown. Bias can be introduced from many sources, including treatment assignment, ascertainment of endpoints, and methods of analysis. Fortunately, there are proven methods that eliminate bias. If these methods are properly used, the probability of seeing a result more extreme than that observed, under the assumption that treatment has no effect, can be calculated in a definitive way. If this probability or P value is small enough (e.g., , 0.05) it is concluded that the treatment effect is real. A major thrust of this article is to explore the methods by which bias is precluded.