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 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.

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 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].

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.