Comparative efficacy and acceptability of first-generation and second-generation antidepressants in the acute treatment of major depression: protocol for a network meta-analysis

Abstract

[Introduction] Many antidepressants are indicated for the treatment of major depression. Two network meta-analyses have provided the most comprehensive assessments to date, accounting for both direct and indirect comparisons; however, these reported conflicting interpretation of results. Here, we present a protocol for a systematic review and network meta-analysis aimed at updating the evidence base and comparing all second-generation as well as selected first-generation antidepressants in terms of efficacy and acceptability in the acute treatment of major depression. [Methods and analysis] We will include all randomised controlled trials reported as double-blind and comparing one active drug with another or with placebo in the acute phase treatment of major depression in adults. We are interested in comparing the following active agents: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. The main outcomes will be the proportion of patients who responded to or dropped out of the allocated treatment. Published and unpublished studies will be sought through relevant database searches, trial registries and websites; all reference selection and data extraction will be conducted by at least two independent reviewers. We will conduct a random effects network meta-analysis to synthesise all evidence for each outcome and obtain a comprehensive ranking of all treatments. To rank the various treatments for each outcome, we will use the surface under the cumulative ranking curve and the mean ranks. We will employ local as well as global methods to evaluate consistency. We will fit our model in a Bayesian framework using OpenBUGS, and produce results and various checks in Stata and R. We will also assess the quality of evidence contributing to network estimates of the main outcomes with the GRADE framewor