After years of finding that any one treatment does not affect all patients with a disorder in the same way, that a treatment highly effective for some may be ineffective or even harmful for others, and that, statistically significant or not, the effect sizes of many treatments tend to be small, emphasis in randomized clinical trials (RCTs) is gradually shifting (1) to increased focus on effect sizes [1-8] and (2) to discovery and documentation of moderators of treatment choice on outcome in RCTs, that is, personalized medicine [9-13]. In effect, the emphasis is shifting from a focus on populations as a whole to one in which the individual differences among the patients in the population are explicitly acknowledged and dealt with.

However, methodological problems continue to exist. There is often still some disagreement about the definition of a moderator and a lack of a clear distinction between a moderator versus a predictor or a mediator of treatment outcome [14]. Moreover, methods to assess the strength or impact of a moderator (effect size) in a way that is clinically and/or scientifically meaningful are lacking. Preacher and Kelley [15] in discussing similar issues for mediators define an effect size as a measure that reflects a quantity of interest, scaled appropriately so as to be interpretable in the context of interest, a population parameter that can be estimated (with a confidence interval) from a sample so as to be unbiased, consistent, and/or efficient. Moreover, in order to use such an effect size to compare moderators (or mediators) of a treatment choice on an outcome in an RCT, the effect size must be such that one can reasonably compare the effect of one moderator (or mediator) to another for the same treatment choice and outcome.

Here, as a first step, we review the definitions of, and distinctions between, predictors, moderators, and mediators using the MacArthur approach [14, 16, 17] and discuss their importance both to scientific progress and to clinical decision making. We will then use the classic linear regression model for continuous outcome measures in the Baron and Kenny seminal paper on moderators and mediators [18] from which the MacArthur approach evolved in order to develop one moderator effect size with wide, but not universal, applicability, one that carries statistical meaning but does not necessarily well reflect clinical significance. However, this approach is the one most commonly used and is very useful both in exploring for moderators and in seeking an optimal composite moderator by linearly combining individual moderators. Because each individual moderator is likely to have a small effect, such optimal composite moderators are crucial for clinical decision making.