Premium
Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach
Author(s) -
Kraemer Helena Chmura
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5734
Subject(s) - moderation , randomized controlled trial , treatment effect , outcome (game theory) , clinical trial , psychology , affect (linguistics) , medicine , null hypothesis , clinical psychology , econometrics , social psychology , mathematics , traditional medicine , mathematical economics , communication
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. Copyright © 2013 John Wiley & Sons, Ltd.