Predicting optimal treatment allocation for cognitive analytic-guided self-help versus cognitive behavioural-guided self-help.

Objectives: Given the ubiquity in routine services of low-intensity guided self-help (GSH) psychological interventions, better patient selection for these brief interventions would be organizationally efficient. This study therefore sought to define who would respond best to two different types of GSH for anxiety to enable better future treatment matching.

Methods: The study used outcome data from a patient preference trial (N = 209) comparing cognitive analytic therapy-guided self-help (CAT-GSH) with cognitive behavioural therapy-guided self-help (CBT-GSH). Elastic Net regularization and Boruta random forest variable selection methods were applied. Regression models calculated the patient advantage index (PAI) to designate which GSH was likely the most effective for each patient. Outcomes were compared for those receiving their PAI-indicated optimal and non-optimal GSH.

Results: Lower baseline depression and anxiety severity predicted better outcomes for both types of GSH. Patient preference status was not associated with outcome during either GSH. Sixty-three % received their model indicating optimal GSH and these had significantly higher rates of reliable and clinically significant reductions in anxiety at both post-treatment (35.9% vs. 16.6%) and follow-up (36.6% vs. 19.2%). No single patient with a large PAI had a reliable and clinically significant reduction in anxiety at post-treatment or follow-up when they did not receive their optimal GSH.

Conclusions: Treatment matching algorithms have the potential to support evidenced-based treatment selection for GSH. Treatment selection and supporting patient choice needs to be integrated. Future research needs to investigate the use of the PAI for GSH treatment matching, but with larger and more balanced samples.