Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy

ObjectiveTo develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a full data model using multiple trial-based and routine variables, and (2) a routine data model using only variables available in the English NHS Talking Therapies program.MethodData was used from the PRaCTICED trial comparing PCET vs. CBT for 255 patients meeting a diagnosis of moderate or severe depression. Separate full and routine data models were derived and the latter tested in an external data sample.ResultsThe full data model provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's d = .65 [.40, .91]) and 12 months (d = .85 [.59, 1.10]) post-randomization. The routine data model performed similarly in the training and test samples with non-significant effect sizes, d = .19 [-.05, .44] and d = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (d >= 0.3), the resulting effect size was significant, d = .38 [.11, 64].ConclusionA treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.

Automated summary

Two prediction algorithms were developed to recommend person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression. The full data model provided better prediction results, while the routine data model showed similar performance in the training and test samples. Targeted matching resulted in larger effects for patients with the strongest treatment match.