The comparative evidence basis for the efficacy of second-generation antidepressants in the treatment of depression in the US: A Bayesian meta-analysis of Food and Drug Administration reviews

Background: Studies have shown similar efficacy of different antidepressants in the treatment of depression. Method: Data of phase-2 and -3 clinical-trials for 16 antidepressants (levomilnacipran, desvenlafaxine, duloxetine, venlafaxine, paroxetine, escitalopram, vortioxetine, mirtazapine, venlafaxine XR, sertraline, fluoxetine, citalopram, paroxetine CR, nefazodone, bupropion, vilazodone), approved by the FDA for the treatment of depression between 1987 and 2016, were extracted from the FDA reviews that were used to evaluate efficacy prior to marketing approval, which are less liable to reporting biases. Meta-analytic Bayes factors, which quantify the strength of evidence for efficacy, were calculated. In addition, posterior pooled effect-sizes were calculated and compared with classical estimations. Results: The resulted Bayes factors showed that the evidence load for efficacy varied strongly across antidepressants. However, all tested drugs except for bupropion and vilazodone showed strong evidence for their efficacy. The posterior effect-size distributions showed variation across antidepressants, with the highest pooled estimated effect size for venlafaxine followed by paroxetine, and the lowest for bupropion and vilazodone. Limitations: Not all published trials were included in the study. Conclusions: The results illustrate the importance of considering both the effect size and the evidence-load when judging the efficacy of a treatment. In doing so, the currently employed Bayesian approach provided clear insights on top of those gained with traditional approaches.