Some General Statistical Concepts

Although we assume some familiarity with basic statistical concepts and space does not permit a detailed account, we review here some concepts vital to the design and analysis of clinical trials and associated studies. In the classical (e.g., fre-quentist) statistical hypothesis testing paradigm, a quantity referred to as type II or b error equals the probability that a statistical test fails to produce a decision in favor of a treatment effect when in fact the effect is manifest in the population. The complement of this probability (1 - b) is referred to as statistical power, and equals the probability of correctly detecting a treatment effect. Statistical power depends on the other principal parameters in hypothesis testing, specifically, the probability of incorrectly finding in favor of an effect when none exists (discussed in a following section), the sample size, and the size of the treatment effect. It is imperative that clinical trials be designed with adequate statistical power, typically 0.80 or greater for anticipated treatment effects that are both realistic and clinically material, so as not to obtain equivocal findings concerning the potential worth of a new treatment under consideration. Studies with low statistical power can cause delay in development or even abandonment of promising treatments and waste valuable resources, not least of which is the participation and goodwill of patients.3 In contrast, an adequately powered trial that does

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