A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression
A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression
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DOI：
10.1080/10503307.2018.1563312
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发表时间：
2020-02-17
影响因子：
3.9
通讯作者：
Driessen, Ellen
中科院分区：
文献类型：
--
作者：
Cohen, Zachary D.;Kim, Thomas T.;Driessen, Ellen
目的：我们使用一种新的变量选择程序进行治疗选择，该程序根据轻度至中度抑郁症成人患者的治疗前特征，决定在认知行为治疗（CBT）和心理动力治疗（PDT）之间进行治疗建议。方法：数据来自CBT与PDT治疗抑郁症的随机比较（N = 167, 71%为女性，平均年龄= 39.6）。该方法结合了四种不同的统计技术来确定与差异治疗反应一致的患者特征。各种变量结合起来，产生预测，表明每个人的最佳待遇。接受指示治疗的患者与未接受指示治疗的患者的平均结果进行回顾性比较，以估计模型效用。结果：在检验的49个预测因子中，抑郁严重程度、焦虑敏感性、外向性和心理治疗需求被纳入最终模型。接受适应症治疗的患者与未接受适应症治疗的患者相比，治疗后平均汉密尔顿抑郁评定量表得分低1.6分（95%CI = [0.5:2.8];d = 0.21）。在接受最强治疗建议的60%患者中，这一优势增加到2.6 （95%CI = [1.4:3.7];d = 0.37）。结论：变量选择程序在描述预测变量的重要性方面有所不同。在构建治疗选择模型时，关注一致指示的预测因子可能是明智的。较小的N和缺乏单独的验证样本表明，在使用该模型之前需要进行前瞻性测试。
Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT).Method: Data are drawn from a randomized comparison of CBT versus PDT for depression (N = 167, 71% female, mean-age = 39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual's optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility.Results: Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI = [0.5:2.8];d = 0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI = [1.4:3.7];d = 0.37).Conclusions: Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small N and lack of separate validation sample indicate a need for prospective tests before this model is used.