What Information Will a Statistician Need to Help Me With a Sample Size Calculation?

s an investigator, one of the most pressing questions during the planning phase of a clinical trial is the sample size. It is desirable and necessary to include your statistician in the discussion of trial design well before the topic of sample size is broached, particularly in the case of innovative or adaptive designs. Although there are a multitude of available designs, we will focus on the 2-arm, superiority trial design wherein 2 groups of subjects are compared with respect to a given outcome of interest. The foundation of a trial design is the hypothesis of interest. At the end of the trial, one of two conclusions is possible: (1) there is sufficient evidence that the 2 groups are different (ie, reject the null hypothesis) or (2) there is NOT sufficient evidence that the 2 groups are different (ie, fail to reject the null hypothesis). Given this hypothesis, the first step is to define a primary end point that will be used to test the hypothesis. For example, if the null hypothesis states that there is no difference in the proportion of deaths between the treatment and control group, then the primary end point must be the proportion of deaths. Once the primary end point has been identified, it is important to discuss the trial design that will be used to collect information on the primary outcome (eg, parallel, factorial, or crossover designs). For the purpose of this article, it is assumed that investigators have already discussed these design issues with the statistician and are now ready to calculate the required sample size given the design parameters. We begin with the key components of a sample size calculation—statistical significance level and clinical effect size; we then expand the discussion with inflation of the sample size for protocol nonadherence and additional concerns for studies using adaptive designs. All power calculations can be generalized to consisting of 2 components, the degree of certainty with which to test the stated hypothesis (statistical significance and power) and the expected difference between the 2 treatment arms measured in the trial (clinical effect size). When the clinical effect size is small or the variability in the effect is large, the sample size of the trial must increase to accurately reflect the population. By sampling, we assume that the sample is representative of the population. If our assumption is correct, then the result observed in the trial …