What next? A Bayesian hierarchical modeling re-examination of treatments for adolescents with selective serotonin reuptake inhibitor-resistant depression

Background: Psychiatrists frequently struggle with how to sequence treatment for depressed adolescents who do not respond to an adequate trial of a selective serotonin reuptake inhibitor (SSRI). This study leveraged recent statistical and computational advances to create Bayesian hierarchal models (BHMs) of response in the treatment of SSRI-resistant depression in adolescents study to inform treatment planning.

Methods: BHMs of individual treatment trajectories were developed and estimated using Hamiltonian Monte Carlo no u-turn sampling. From the Monte Carlo pseudorandom sample, 95% credible intervals, means, posterior tail probabilities, and so forth, were determined. Then, for the random effects model, posterior tail probabilities were used to create Bayesian two-tailed p values to evaluate the null hypotheses: no difference in efficacy between SSRIs and venlafaxine. The robustness of the results was examined using the fixed effects model of treatment comparisons.

Results: In patients not receiving cognitive behavioral therapy (CBT; n = 168), SSRIs produced greater and faster improvement in depressive symptoms compared to venlafaxine (p = .015). No differences in response or trajectory of response for symptoms of anxiety were detected between SSRIs and venlafaxine (p = .168). For patients receiving CBT (n = 162), SSRIs and venlafaxine produced similar improvements in symptoms of anxiety and depression.

Conclusions: Findings from this novel computational approach suggest that a second trial of an SSRI is warranted for depressed adolescents who fail to respond to initial SSRI treatment.