Current trends in the cardiovascular clinical trial arena (I)

The existence of effective therapies for most cardiovascular disease states, coupled with increased requirements that potential benefits of new drugs be evaluated on clinical rather than surrogate endpoints, makes it increasingly difficult to substantiate any incremental improvements in efficacy that these new drugs might offer. Compounding the problem is the highly controversial issue of comparing new agents with placebos rather than active pharmaceuticals in drug efficacy trials. Despite the recent consensus that placebos may be used ethically in well-defined, justifiable circumstances, the problem persists, in part because of increased scrutiny by ethics committees but also because of considerable lingering disagreement regarding the propriety and scientific value of placebo-controlled trials (and trials of antihypertensive drugs in particular). The disagreement also substantially affects the most viable alternative to placebo-controlled trials: actively controlled equivalence/noninferiority trials. To a great extent, this situation was prompted by numerous previous trials of this type that were marked by fundamental methodological flaws and consequent false claims, inconsistencies, and potential harm to patients. As the development and use of generic drugs continue to escalate, along with concurrent pressure to control medical costs by substituting less-expensive therapies for established ones, any claim that a new drug, intervention, or therapy is "equivalent" to another should not be accepted without close scrutiny. Adherence to proper methods in conducting studies of equivalence will help investigators to avoid false claims and inconsistencies. These matters will be addressed in the third article of this three-part series

Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations

Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so – and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the Pvalues they produce) can lead to small P-values even if the declared test hypothesis is correct, and can lead to large P-values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P-values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting