Department of Psychiatry and Behavioral Sciences, Stanford University, 1116 Forest Avenue, Palo Alto, CA 94301-3032, USA.
Stat Med. 2013 May 20;32(11):1964-73. doi: 10.1002/sim.5734. Epub 2013 Jan 10.
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.
没有一种治疗方法可能以同样的方式对所有患有某种疾病的患者都有效。一种对某些人非常有效的治疗方法可能对其他人无效甚至有害。无论是否具有统计学意义，许多治疗方法的效果大小往往都很小。因此，临床研究的重点正在逐渐转移（1）更多地关注效果大小，（2）在随机临床试验中发现和记录治疗效果的调节因素，即个性化医学，其中明确承认患者之间的个体差异。如何检验治疗结果调节的零假设是相当已知的。这里的重点是在参数假设下，如何定义调节的强度，即调节效果大小，无论是出于科学目的还是出于评估临床意义，以便比较调节因素并从中选择，并开发一个综合调节因素，该因素可能比任何单一的调节因素更能调节治疗对结果的影响，最终为临床医生提供指导，告知他们应该给哪些患者开哪些治疗方法。
