Department of Psychology, University of Trier, Trier, Germany.
Department of Psychology, University of Haifa, Haifa, Israel.
Psychother Res. 2020 Mar;30(3):300-309. doi: 10.1080/10503307.2019.1597994. Epub 2019 Mar 26.
Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, however, about their utility in process-outcome research. This study is the first to apply personalized treatment methods in the field of process-outcome research, as demonstrated based on the alliance-outcome association. Using a sample of 741 patients, individual regressions were fitted to estimate within-patient effects of the alliance-outcome association. The Boruta algorithm was used to identify patient intake characteristics that moderate the within-patient alliance-outcome association. The nearest neighbor approach was used to identify patients whose relevant pretreatment characteristics were similar to those of a target patient. The alliance-outcome associations of the most similar patients were subsequently used to predict the alliance-outcome association of the target patient. Irrespective of the number of selected nearest neighbors, the correlation between the observed and predicted alliance-outcome associations was low and insignificant. According to the true error of the prediction, the demonstrated approach was unable to improve predictions made with a simple comparison model. The study demonstrated the application of personalized treatment methods in process-outcome research and opens many new paths for future research.
个性化治疗方法在医学和心理健康的许多领域的疗效研究中显示出巨大的潜力。然而，关于它们在过程-结果研究中的实用性知之甚少。本研究是首次将个性化治疗方法应用于过程-结果研究，基于联盟-结果关联来证明。本研究使用了 741 名患者的样本，通过个体回归来估计联盟-结果关联的个体内效应。使用 Boruta 算法来识别调节个体内联盟-结果关联的患者摄入特征。使用最近邻方法来识别那些与目标患者的相关预处理特征相似的患者。随后，将最相似患者的联盟-结果关联用于预测目标患者的联盟-结果关联。无论选择的最近邻居数量如何，观察到的和预测的联盟-结果关联之间的相关性都很低且不显著。根据预测的真实误差，所展示的方法无法改进简单比较模型的预测。该研究展示了个性化治疗方法在过程-结果研究中的应用，并为未来的研究开辟了许多新的途径。
