Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach.

No one treatment is likely to affect all patients with a disorder in the same way. A treatment highly effective for some may be ineffective or even harmful for others. Statistically significant or not, the effect sizes of many treatments tend to be small. Consequently, emphasis in clinical research is gradually shifting (1) to increased focus on effect sizes and (2) to discovery and documentation of moderators of treatment effect on outcome in randomized clinical trials, that is, personalized medicine, in which individual differences between patients are explicitly acknowledged. How to test a null hypothesis of moderation of treatment outcome is reasonably well known. The focus here is on how, under parametric assumptions, to define the strength of moderation, that is, a moderator effect size, either for scientific purposes or for assessment of clinical significance, in order to compare moderators and choose among them and to develop a composite moderator, which might more strongly moderate the effect of a treatment on outcome than any single moderator that might ultimately provide guidance for clinicians as to whom to prescribe what treatment.