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Category: Project

Description: Objective: Treatment selection could improve outcomes by helping individuals select their optimal intervention. We refine the Personalized Advantage Index approach to generate individual treatment recommendations based on pre-treatment characteristics for adults with depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method: Data were drawn from a randomized comparison of CBT versus PDT in a sample of 167 individuals with depression. We introduce a novel method combining four different statistical techniques to identify consistent patient characteristics associated with treatment outcome. We combined these variables to generate predictions indicating the optimal treatment for each patient. We assessed retrospectively the effectiveness of our model by comparing the average treatment outcomes for the patients who received their indicated treatment versus those who did not. Results: Depression severity, anxiety sensitivity, extraversion, and psychological treatment needs were found to predict differential treatment efficacy. The average post-treatment Hamilton Depression Rating Scale scores 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 points (95%CI=[1.4:3.7]; d=0.37). Treatment recommendations were improved by combining different statistical techniques to identify moderators of treatment response rather than relying on one method. Conclusions: Patient characteristics could help individuals choose between CBT and PDT. The small sample and lack of a separate validation sample indicate the need for prospective tests before using this model for treatment selection. These findings contribute to a growing literature on model-guided treatment recommendations in depression.