Size of treatment effects and their importance to clinical research and practice

In randomized clinical trails (RCTs), effect sizes seen in earlier studies guide both the choice of the effect size that sets the appropriate threshold of clinical significance and the rationale to believe that the true effect size is above that threshold worth pursuing in an RCT. That threshold is used to determine the necessary sample size for the proposed RCT. Once the RCT is done, the data generated are used to estimate the true effect size and its confidence interval. Clinical significance is assessed by comparing the true effect size to the threshold effect size. In subsequent meta-analysis, this effect size is combined with others, ultimately to determine whether treatment (T) is clinically significantly better than control (C). Thus, effect sizes play an important role both in designing RCTs and in interpreting their results; but specifically which effect size? We review the principles of statistical significance, power, and meta-analysis, and commonly used effect sizes. The commonly used effect sizes are limited in conveying clinical significance. We recommend three equivalent effect sizes: number needed to treat, area under the receiver operating characteristic curve comparing T and C responses, and success rate difference, chosen specifically to convey clinical significance.