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

Objective: To develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a using multiple trial-based and routine variables, and (2) a using only variables available in the English NHS Talking Therapies program.

Method: Data 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.

Results: The provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's  = .65 [.40, .91]) and 12 months ( = .85 [.59, 1.10]) post-randomization. The performed similarly in the training and test samples with non-significant effect sizes,  = .19 [-.05, .44] and  = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (≥ 0.3), the resulting effect size was significant,  = .38 [.11, 64].

Conclusion: A treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.