COMPARATIVE STUDY
JOURNAL ARTICLE
META-ANALYSIS
RESEARCH SUPPORT, NON-U.S. GOV'T
Add like
Add dislike
Add to saved papers

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

BMJ Open 2016 July 9
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 Published and unpublished studies will be sought through relevant database searches, trial registries and websites; all reference selection and data extraction will be conducted by at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 framework.

ETHICS AND DISSEMINATION: This review does not require ethical approval.

PROSPERO REGISTRATION NUMBER: CRD42012002291.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app