Clinical trials in cardiovascular medicine in an era of marginal benefit, bias, and hyperbole.

Clinical trials in the modern medical era began in the mid-20th century with the first randomized, doubleblind assessment of an antibiotic, streptomycin, for the treatment of pulmonary tuberculosis.1 Over the course of the next 50 years, the randomized, controlled trial became the “gold standard” for testing the efficacy of new therapies and is generally required for their approval by regulatory agencies worldwide. To be sure, many effective cardiovascular therapies have been identified and approved for use following this generally accepted approach. In this editorial, I will review the many factors that can and do adversely affect the performance and interpretation of contemporary clinical trials in cardiovascular medicine, hoping to shed light on what I view as a series of growing and only partly remediable challenges to effective clinical investigation. Clinical trials in cardiovascular medicine set the standard for randomized, controlled trial design owing to the population prevalence of the major cardiovascular diseases, the event rates in these patient populations, and the practicably achievable sample sizes sufficient to minimize type II errors. In the early studies of fibrinolytic therapy for myocardial infarction (ISIS [International Study of Infarct Survival] and TIMI [Thrombolysis In Myocardial Infarction]), for example, the expected 5-week mortality rate for conventionally treated patients was 12% to 13% at the time2,3 in Europe and the United States. Thus, with several thousand patients in each treatment arm, one could expect to detect confidently true reductions in absolute mortality of 3% to 4%, as was the case in these landmark trials. Over the past three decades, mortality rates for highly prevalent cardiovascular diseases, including acute coronary syndromes, heart failure, and sudden death, have continuously improved owing to the clear benefits of therapies proved to be efficacious in double-blind, randomized, controlled trials. With these mounting, cumulative successes, however, the marginal benefit of any proposed intervention decreases. Realistic limits, both operational and financial, to the size of study samples decrease the statistical power and the absolute treatment effect detectable in these trials. To appreciate the implications of narrowing clinical margins for designing and interpreting clinical cardiovascular trials, we must digress to discuss a few relevant statistical principles, including the application of Bayesian analysis to trial design. Although statistical power and significance are wellknown determinants of the probability that a research observation is true, another important factor to consider is the prior (prestudy) probability that the observation is true.4,5 According to Ioannidis,6 the likelihood that a statistically significant research finding is more likely true than false is defined by the inequality (1 ) R , where is the type II error rate, (1 ) is the statistical power, is the type I error rate, and R is the prestudy odds of a true relationship to no relationship [which defines the prestudy probability, R/(R 1)]. As R increases above 1, the prestudy probability approaches 1, and satisfying the inequality requires less statistical power; however, as R decreases below 1, the prestudy probability approaches 0, and satisfying the inequality requires increasing statistical power. In all cases, R must be greater than to satisfy the inequality. Put in more practical terms, the higher the prestudy probability that a treatment is effective, the smaller the sample size required to prove its benefit. Although this statement is intuitively obvious, its implications for trial design are interesting to contemplate. Clearly, a reasonable assessment of prestudy probability can influence sample size calculations greatly (and, in the absence of preliminary data, estimates of effect size may implicitly incorporate prestudy probability). Ideally, high prestudy probabilities are preferred because they limit sample sizes required for clinical trials, whether they are estimated directly or incorporated implicitly in sample size estimates. Even in the setting of a high prestudy probability, however, a small sample size compromises the precision of the estimated treatment effect. We should not lose sight of an interesting ethical trap that a high prestudy probability yields for clinical trialists, viz, it minimizes clinical equipoise or the belief by the trialist that all possible trial outcomes are equally likely to occur. Without clinical equipoise, subjects enrolled in the placebo or standard treatment arm of a therapeutic trial are, one can argue, unnecessarily exposed to substandard therapy. Obviously, if the prestudy probability of benefit of an intervention is very high, there is not only no ethical justification for enrolling subjects in the control arm of the trial but also no reason to perform the trial in the first place. Thus, one needs to balance a prestudy probability that is greater than 0.5 (but not too much greater) with the cost of an excessively sized trial and the importance of defining the benefit of an as yet unproved therapy for the affected population. In a trial of a highly effective standard therapy compared with the added effect of a new therapy, considerations of truly marginal From the Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass. Correspondence to Joseph Loscalzo, MD, PhD, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115. (Circulation 2005;112:3026-3029.) © 2005 American Heart Association, Inc.