Abstract

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done.

Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset.

Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn’t improve the results.

Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.

摘要

目的：优化治疗选择可以改善抑郁症的治疗效果。一种有前途的方法是个性化优势指数（PAI），它可以预测给定个体的最佳治疗方法。为了确定PAI的可推广性，需要对模型进行外部验证，而这很少进行。

方法： PAI模型是在两个独立试验中的每个试验中开发的，研究之间存在重大差异，两者均比较了抑郁症的CBT和IPT（STEPd：n  = 151和FreqMech：n  = 200）。随后，在另一个数据集中测试了两个PAI模型。

结果：在STEPd研究中，分配给PAI指示治疗的个体与未分配PAI指示治疗的个体之间的治疗后抑郁显着不同（d  = .57）。在FreqMech研究中，接受指定治疗的患者与未接受指定治疗的患者之间的治疗后抑郁无显着差异（d  = .20）。交叉试验预测表明，接受指定治疗的患者与未接受非指定治疗的患者之间的治疗后抑郁无显着差异（d  = .16和d = .27）。敏感性分析表明，仅基于重叠变量的交叉试验预测不会改善结果。

结论： PAI的外部验证结果有限，并强调研究之间的差异和许多其他挑战。