The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Comparative Efficacy and Acceptability of 21 Antidepressant Drugs for the Acute Treatment of Adults With Major Depressive Disorder: A Systematic Review and Network Meta-Analysis
Open accessAbstract
Background: Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide in adults. Pharmacological and non-pharmacological treatments are available; however, because of inadequate resources, antidepressants are used more frequently than psychological interventions. Prescription of these agents should be informed by the best available evidence. Therefore, we aimed to update and expand our previous work to compare and rank antidepressants for the acute treatment of adults with unipolar major depressive disorder. Methods: We did a systematic review and network meta-analysis. We searched Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, PsycINFO, the websites of regulatory agencies, and international registers for published and unpublished, double-blind, randomised controlled trials from their inception to Jan 8, 2016. We included placebo-controlled and head-to-head trials of 21 antidepressants used for the acute treatment of adults (≥18 years old and of both sexes) with major depressive disorder diagnosed according to standard operationalised criteria. We excluded quasi-randomised trials and trials that were incomplete or included 20% or more of participants with bipolar disorder, psychotic depression, or treatment-resistant depression; or patients with a serious concomitant medical illness. We extracted data following a predefined hierarchy. In network meta-analysis, we used group-level data. We assessed the studies’ risk of bias in accordance to the Cochrane Handbook for Systematic Reviews of Interventions, and certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. Primary outcomes were efficacy (response rate) and acceptability (treatment discontinuations due to any cause). We estimated summary odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with PROSPERO, number CRD42012002291. Findings: We identified 28 552 citations and of these included 522 trials comprising 116 477 participants. In terms of efficacy, all antidepressants were more effective than placebo, with ORs ranging between 2.13 (95% credible interval [CrI] 1.89–2.41) for amitriptyline and 1.37 (1.16–1.63) for reboxetine. For acceptability, only agomelatine (OR 0.84, 95% CrI 0.72–0.97) and fluoxetine (0.88, 0.80–0.96) were associated with fewer dropouts than placebo, whereas clomipramine was worse than placebo (1.30, 1.01–1.68). When all trials were considered, differences in ORs between antidepressants ranged from 1.15 to 1.55 for efficacy and from 0.64 to 0.83 for acceptability, with wide CrIs on most of the comparative analyses. In head-to-head studies, agomelatine, amitriptyline, escitalopram, mirtazapine, paroxetine, venlafaxine, and vortioxetine were more effective than other antidepressants (range of ORs 1.19–1.96), whereas fluoxetine, fluvoxamine, reboxetine, and trazodone were the least efficacious drugs (0.51–0.84). For acceptability, agomelatine, citalopram, escitalopram, fluoxetine, sertraline, and vortioxetine were more tolerable than other antidepressants (range of ORs 0.43–0.77), whereas amitriptyline, clomipramine, duloxetine, fluvoxamine, reboxetine, trazodone, and venlafaxine had the highest dropout rates (1.30–2.32). 46 (9%) of 522 trials were rated as high risk of bias, 380 (73%) trials as moderate, and 96 (18%) as low; and the certainty of evidence was moderate to very low. Interpretation: All antidepressants were more efficacious than placebo in adults with major depressive disorder. Smaller differences between active drugs were found when placebo-controlled trials were included in the analysis, whereas there was more variability in efficacy and acceptability in head-to-head trials. These results should serve evidence-based practice and inform patients, physicians, guideline developers, and policy makers on the relative merits of the different antidepressants. (Reprinted with permission from Lancet 2018; 391:1357-66)
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Short-term use of low to moderate doses of antidepressants, and particularly of SSRIs, is generally well tolerated [14], and notably in older adults [15][16][17].”
From Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study (2021) · cites this paper as [14]
Citations
Cites
Salanti, G., Ades, A. E., & Ioannidis, J. P. A. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.03.016 · Cited by 3,766
Self-citationChecking consistency in mixed treatment comparison meta-analysis
Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010) · Statistics in Medicine · doi:10.1002/sim.3767 · Cited by 2,521
Graphical tools for network meta-analysis in STATA
Chaimani, A., Higgins, J. P. T., Mavridis, D., Spyridonos, P., & Salanti, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076654 · Cited by 2,473
Self-citationSelective publication of antidepressant trials and its influence on apparent efficacy
Turner, E. H., Matthews, A. M., Linardatos, E., Tell, R. A., & Rosenthal, R. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa065779 · Cited by 2,435
Self-citationThapar, A., Collishaw, S., Pine, D. S., & Thapar, A. K. (2012) · The Lancet · doi:10.1016/s0140-6736(11)60871-4 · Cited by 2,365
Kassebaum, N. J., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, J., Carter, A., Casey, D. C., Charlson, F. J., Coates, M. M., Coggeshall, M., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31460-x · Cited by 2,163
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies
Higgins, J. P. T., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., & White, I. R. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.1044 · Cited by 2,098
Self-citationMurray, C. J. L., Barber, R. M., Foreman, K. J., Özgören, A. A., Abd-Allah, F., Abera, S. F., Aboyans, V., Abraham, J. P., Abubakar, I., Abu-Raddad, L. J., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)61340-x · Cited by 1,966
The BUGS project: evolution, critique and future directions
Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009) · Statistics in Medicine · doi:10.1002/sim.3680 · Cited by 1,876
Cipriani, A., Furukawa, T. A., Salanti, G., Geddes, J. R., Higgins, J. P., Churchill, R., Watanabe, N., Nakagawa, A., Omori, I. M., McGuire, H., et al. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60046-5 · Cited by 1,654
Self-citationEvaluating the quality of evidence from a network meta-analysis
Salanti, G., Del Giovane, C., Chaimani, A., Caldwell, D. M., & Higgins, J. P. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099682 · Cited by 1,402
Self-citationCipriani, A., Zhou, X., Del Giovane, C., Hetrick, S. E., Qin, B., Whittington, C., Coghill, D., Zhang, Y., Hazell, P., Leucht, S., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30385-3 · Cited by 734
Self-citationHow do antidepressants work? New perspectives for refining future treatment approaches
Harmer, C. J., Duman, R. S., & Cowen, P. J. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30015-9 · Cited by 616
Hróbjartsson, A., Thomsen, A. S. S., Emanuelsson, F., Tendal, B., Hilden, J., Boutron, I., Ravaud, P., & Brorson, S. (2013) · Canadian Medical Association Journal · doi:10.1503/cmaj.120744 · Cited by 527
Rhodes, K. M., Turner, R., & Higgins, J. P. T. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.08.012 · Cited by 398
Self-citationImputing response rates from means and standard deviations in meta-analyses
Furukawa, T. A., Cipriani, A., Barbui, C., Brambilla, P., & Watanabe, N. (2005) · International Clinical Psychopharmacology · doi:10.1097/00004850-200501000-00010 · Cited by 337
Self-citationVisualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package
Chaimani, A., & Salanti, G. (2015) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1501500402 · Cited by 323
Self-citationPerlis, R. H., Perlis, C. S., Wu, Y., Hwang, C., Joseph, M., & Nierenberg, A. A. (2005) · American Journal of Psychiatry · doi:10.1176/appi.ajp.162.10.1957 · Cited by 318
A Model of Placebo Response in Antidepressant Clinical Trials
Rutherford, B. R., & Roose, S. P. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12040474 · Cited by 268
Effectiveness of antidepressants: an evidence myth constructed from a thousand randomized trials?
Ioannidis, J. P. (2008) · Philosophy, Ethics, and Humanities in Medicine · doi:10.1186/1747-5341-3-14 · Cited by 253
Self-citationFurukawa, T. A., Cipriani, A., Atkinson, L. Z., Leucht, S., Ogawa, Y., Takeshima, N., Hayasaka, Y., Chaimani, A., & Salanti, G. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30307-8 · Cited by 219
Self-citationFurukawa, T. A., Salanti, G., Atkinson, L. Z., Leucht, S., Ruhe, H. G., Turner, E. H., Chaimani, A., Ogawa, Y., Takeshima, N., Hayasaka, Y., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010919 · Cited by 214
Self-citationNetwork meta-analysis: a norm for comparative effectiveness?
Higgins, J. P. T., & Welton, N. J. (2015) · The Lancet · doi:10.1016/s0140-6736(15)61478-7 · Cited by 133
Self-citationWalkup, J. T. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16091059 · Cited by 112
Evaluating novel agent effects in multiple-treatments meta-regression
Salanti, G., Dias, S., Welton, N. J., Ades, A. E., Golfinopoulos, V., Kyrgiou, M., Mauri, D., & Ioannidis, J. P. A. (2010) · Statistics in Medicine · doi:10.1002/sim.4001 · Cited by 111
Self-citationSchwarzer, G., Carpenter, J. R., & Rücker, G. (2015) · Use R! · doi:10.1007/978-3-319-21416-0_8 · Cited by 109
Marginal analysis of incomplete longitudinal binary data: a cautionary note on LOCF imputation
Cook, R. J., Zeng, L., & Yi, G. Y. (2004) · Biometrics · doi:10.1111/j.0006-341x.2004.00234.x · Cited by 88
Meta-analyses Can Be Credible and Useful
Ioannidis, J. P. A. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0035 · Cited by 36
Self-citationStrategic use of new generation antidepressants for depression: SUN(^_^)D study protocol
Furukawa, T. A., Akechi, T., Shimodera, S., Yamada, M., Miki, K., Watanabe, N., Inagaki, M., & Yonemoto, N. (2011) · Trials · doi:10.1186/1745-6215-12-116 · Cited by 23
Self-citationFurukawa, T. A., Miura, T., Chaimani, A., Leucht, S., Cipriani, A., Noma, H., Mitsuyasu, H., Kanba, S., & Salanti, G. (2016) · BMC Research Notes · doi:10.1186/s13104-016-2019-1 · Cited by 23
Self-citationThe need for trial identifiers
Wager, E. (2004) · Current Medical Research and Opinion · doi:10.1185/030079903125002883 · Cited by 13
Research works printed but not linked: 1
- WHO. Depression, 2017, fact sheet
Cited by
Sorted by citations
Kandola, A., Ashdown-Franks, G., Hendrikse, J., Sabiston, C. M., & Stubbs, B. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.040 · Cited by 1,309
Singh, B., Olds, T., Curtis, R., Dumuid, D., Virgara, R., Watson, A., Szeto, K., O'Connor, E., Ferguson, T., Eglitis, E., et al. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2022-106195 · Cited by 1,049
Popova, V., Daly, E. J., Trivedi, M., Cooper, K., Lane, R., Lim, P., Mazzucco, C., Hough, D., Thase, M. E., Shelton, R. C., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19020172 · Cited by 904
Marx, W., Penninx, B. W. J. H., Solmi, M., Furukawa, T. A., Firth, J., Carvalho, A. F., & Berk, M. (2023) · Nature Reviews Disease Primers · doi:10.1038/s41572-023-00454-1 · Cited by 910
Self-citationMalhi, G. S., Bell, E., Bassett, D., Boyce, P., Bryant, R., Hazell, P., Hopwood, M., Lyndon, B., Mulder, R., Porter, R., et al. (2021) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867420979353 · Cited by 614
Metadata
- Authors
- Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., Egger, M., Takeshima, N., Hayasaka, Y., Imai, H., Shinohara, K., Tajika, A., Ioannidis, J. P. A., & Geddes, J. R.
- Affiliations
- Oregon Health & Science University (US)
- Corresponding author
- Cipriani, Andrea
- ORCID
- Venue
- Focus
- Published
- 2018-10
- Volume
- 16
- Issue
- 4
- Pages
- 420-429
- ISSN
- 1541-4094 (ISSN-L)
- 1541-4108
- PMC
- PMC6996085
- OpenAlex
- W3210887564
- Cited by
- 328
- References
- 31
- Citations by year
- 2026: 17 · 2025: 36 · 2024: 52 · 2023: 61 · 2022: 42 · 2021: 64 · 2020: 27 · 2019: 21 · 2018: 7
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- FOCUS The Journal of Lifelong Learning in Psychiatry (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (BRC‐1215‐20005)
- Japan Society for the Promotion of Science (17K19808)
- Topics
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- major depressive disorder, antidepressants, acute treatment, antidepressant efficacy, treatment discontinuation, placebo response, amitriptyline, reboxetine, agomelatine, fluoxetine, venlafaxine, escitalopram, mirtazapine, paroxetine, sertraline, bupropion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review