Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania2Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
JAMA Psychiatry. 2013 Nov;70(11):1241-7. doi: 10.1001/jamapsychiatry.2013.1960.
Identifying treatment moderators may help mental health practitioners arrive at more precise treatment selection for individual patients and can focus clinical research on subpopulations that differ in treatment response.
To demonstrate a novel exploratory approach to moderation analysis in randomized clinical trials.
DESIGN, SETTING, AND PARTICIPANTS: A total of 291 adults from a randomized clinical trial that compared an empirically supported psychotherapy with selective serotonin reuptake inhibitor (SSRI) pharmacotherapy as treatments for depression.
We selected 8 relatively independent individual moderators out of 32 possible variables. A combined moderator, M*, was developed as a weighted combination of the 8 selected individual moderators. M* was then used to identify individuals for whom psychotherapy may be preferred to SSRI pharmacotherapy or vice versa.
Among individual moderators, psychomotor activation had the largest moderator effect size (0.12; 95% CI, <.01 to 0.24). The combined moderator, M*, had a larger moderator effect size than any individual moderator (0.31; 95% CI, 0.15 to 0.46). Although the original analyses demonstrated no overall difference in treatment response, M* divided the study population into 2 subpopulations, with each showing a clinically significant difference in response to psychotherapy vs SSRI pharmacotherapy.
Our results suggest that the strongest determinations for personalized treatment selection will likely require simultaneous consideration of multiple moderators, emphasizing the value of the methods presented here. After validation in a randomized clinical trial, a mental health practitioner could input a patient's relevant baseline values into a handheld computer programmed with the weights needed to calculate M*. The device could then output the patient's M* value and suggested treatment, thereby allowing the mental health practitioner to select the treatment that would offer the greatest likelihood of success for each patient.
识别治疗调节剂可以帮助心理健康从业者为个体患者做出更精确的治疗选择，并将临床研究重点放在治疗反应不同的亚人群上。
展示一种新的探索性方法来进行随机临床试验中的调节分析。
设计、设置和参与者：共有 291 名来自一项随机临床试验的成年人参加了该试验，该试验比较了一种经验支持的心理治疗与选择性 5-羟色胺再摄取抑制剂（SSRI）药物治疗作为抑郁症的治疗方法。
我们从 32 个可能的变量中选择了 8 个相对独立的个体调节剂。一个综合调节剂 M是通过对 8 个选定的个体调节剂进行加权组合而开发的。然后，M用于识别那些可能更倾向于心理治疗而不是 SSRI 药物治疗或反之亦然的个体。
在个体调节剂中，精神运动激活的调节效果最大（0.12；95%置信区间，<.01 至 0.24）。综合调节剂 M的调节效果大于任何个体调节剂（0.31；95%置信区间，0.15 至 0.46）。尽管最初的分析表明治疗反应没有总体差异，但 M将研究人群分为 2 个子群体，每个子群体对心理治疗与 SSRI 药物治疗的反应都有显著的临床差异。
我们的结果表明，个性化治疗选择的最有力决定因素可能需要同时考虑多个调节剂，强调了这里提出的方法的价值。在随机临床试验中得到验证后，心理健康从业者可以将患者的相关基线值输入到一个手持计算机中，该计算机编程有计算 M所需的权重。然后，该设备可以输出患者的 M值和建议的治疗方案，从而使心理健康从业者能够为每个患者选择最有可能成功的治疗方案。
