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Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose-effect relationship using restricted cubic splines. We extend existing models into a dose-effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose-effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose-effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose-effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Approximately 30% of patients experience substantial improvement in depression after 2 months without treatment, and 45% with antidepressants. The smallest worthwhile difference (SWD) refers to an intervention's smallest beneficial effect over a comparison patients deem worthwhile given treatment burdens (harms, expenses and inconveniences), but is undetermined for antidepressants., Objective: Estimating the SWD of commonly prescribed antidepressants for depression compared to no treatment., Methods: The SWD was estimated as a patient-required difference in response rates between antidepressants and no treatment after 2 months. An online cross-sectional survey using Prolific, MQ Mental Health and Amazon Mechanical Turk crowdsourcing services in the UK and USA between October 2022 and January 2023 garnered participants (N=935) that were a mean age of 44.1 (SD=13.9) and 66% women (n=617)., Findings: Of 935 participants, 124 reported moderate-to-severe depressive symptoms but were not in treatment, 390 were in treatment and 495 reported absent-to-mild symptoms with or without treatment experiences. The median SWD was a 20% (IQR=10-30%) difference in response rates for people with moderate-to-severe depressive symptoms, not in treatment, and willing to consider antidepressants, and 25% (IQR=10-35%) for the full sample., Conclusions: Our observed SWDs mean that the current 15% antidepressant benefit over no treatment was sufficient for one in three people to accept antidepressants given the burdens, but two in three expected greater treatment benefits., Implications: While a minority may be satisfied with the best currently available antidepressants, more effective and/or less burdensome medications are needed, with more attention given to patient perspectives., Competing Interests: Competing interests: TAF reports personal fees from Boehringer-Ingelheim, DT Axis, Kyoto University Original, Shionogi, SONY and UpToDate, and a grant from Shionogi, outside the submitted work; In addition, TAF has patents 2020-548587 and 2022-082495 pending, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. ACh has received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation, Lundbeck and Angelini Pharma. He is the CI/PI of two trials about seltorexant in depression, sponsored by Janssen. In the last 3 years SL has received honoraria as a consultant and/or advisor and/or for lectures and/or for educational material from Alkermes, Angelini, Eisai, Gedeon Richter, Janssen, Lundbeck, Medichem, Medscape, Merck Sharpp and Dome, Mitsubishi, Neurotorium, NovoNordisk, Otsuka, Recordati, Roche, Rovi, Sanofi Aventis, TEVA. All the other authors declared no conflict of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ.)

In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics., (© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Background: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question., Methods: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics., Results: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%., Conclusions: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments., (© 2022. The Author(s).)

Objective: The current practice in meta-analysis of the effects of psychopharmacological interventions ignors the administered dose or restricts the analysis in a dose range. This may introduce unnecessary uncertainty and heterogeneity. Methods have been developed to integrate the dose-effect models in meta-analysis., Methods: We describe the two-stage and the one-stage models to conduct a dose-effect meta-analysis using common or random effects methods. We illustrate the methods on a dataset of selective serotonin reuptake inhibitor antidepressants. The dataset comprises 60 randomised controlled trials. The dose-effect is measured on an odds ratio scale and is modelled using restricted cubic splines to detect departure from linearity., Results: The estimated summary curve indicates that the probability of response increases up to 30 mg/day of fluoxetine-equivalent which results in reaching 50% probability to respond. Beyond 40 mg/day, no further increase in the response is observed. The one-stage model includes all studies, resulting in slightly less uncertainty than the two-stage model where only part of the data is analysed., Conclusions: The dose-effect meta-analysis enables clinicians to understand how the effect of a drug changes as a function of its dose. Such analysis should be conducted in practice using the one-stage model that incorporates evidence from all available studies., Competing Interests: Competing interests: TAF reports grants and personal fees from Mitsubishi-Tanabe, personal fees from MSD, grants and personal fees from Shionogi, outside the submitted work. In addition, TAF has a patent 2020-548587concerning smartphone CBT apps pending, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. AC has received research and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Valid assessment of drug efficacy and safety requires an evidence base free of reporting bias. Using trial reports in Food and Drug Administration (FDA) drug approval packages as a gold standard, we previously found that the published literature inflated the apparent efficacy of antidepressant drugs. The objective of the current study was to determine whether this has improved with recently approved drugs., Methods and Findings: Using medical and statistical reviews in FDA drug approval packages, we identified 30 Phase II/III double-blind placebo-controlled acute monotherapy trials, involving 13,747 patients, of desvenlafaxine, vilazodone, levomilnacipran, and vortioxetine; we then identified corresponding published reports. We compared the data from this newer cohort of antidepressants (approved February 2008 to September 2013) with the previously published dataset on 74 trials of 12 older antidepressants (approved December 1987 to August 2002). Using logistic regression, we examined the effects of trial outcome and trial cohort (newer versus older) on transparent reporting (whether published and FDA conclusions agreed). Among newer antidepressants, transparent publication occurred more with positive (15/15 = 100%) than negative (7/15 = 47%) trials (OR 35.1, CI95% 1.8 to 693). Controlling for trial outcome, transparent publication occurred more with newer than older trials (OR 6.6, CI95% 1.6 to 26.4). Within negative trials, transparent reporting increased from 11% to 47%. We also conducted and contrasted FDA- and journal-based meta-analyses. For newer antidepressants, FDA-based effect size (ESFDA) was 0.24 (CI95% 0.18 to 0.30), while journal-based effect size (ESJournals) was 0.29 (CI95% 0.23 to 0.36). Thus, effect size inflation, presumably due to reporting bias, was 0.05, less than for older antidepressants (0.10). Limitations of this study include a small number of trials and drugs-belonging to a single class-and a focus on efficacy (versus safety)., Conclusions: Reporting bias persists but appears to have diminished for newer, compared to older, antidepressants. Continued efforts are needed to further improve transparency in the scientific literature., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: ET previously worked as a Medical Officer for the US Food and Drug Administration (FDA) and reviewed applications submitted by pharmaceutical companies to determine they should be approved for US marketing. He has no financial interest in any pharmaceutical products, approved or otherwise. AC is supported by the National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005). He has received research and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma, outside the submitted work. TAF reports grants and personal fees from Mitsubishi-Tanabe, personal fees from MSD, grants and personal fees from Shionogi, outside the submitted work; in addition, he has a patent 2020-548587 concerning smartphone CBT apps pending, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. GS has received fees paid to the University of Bern for participating in a meeting as real-world evidence and metaanalysis expert from Biogen and Merck. YV has no competing interests.

Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose-response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose-response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose-response meta-analysis model in a simulation study. We found that the Bayesian dose-response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log-log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose-response curve increases between zero dose and 30-40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. HIGHLIGHTS: Network meta-analysis is considered to be a proper way of demonstrating the available evidence, since it allows comparisons between multiple interventions, and has been proved to be statistically powerful. It is challenging to present the voluminous results of NMA in an efficient and comprehendible manner. Evidence evolution based on the relatively new method NMA has not been investigated yet. The results of NMA should not only include the effects but also the confidence in the evidence, which can help interpret the findings appropriately. Effective use of rapidly developing statistical analysis and presentation tools such as Shiny package in R, may facilitate and simplify the visualization of NMA output. We should stay conservative towards new drugs, as their performance was often shown to be exaggerated initially, and it took time to become stable., (© 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Introduction: Matching treatment to specific patients is too often a matter of trial and error, while treatment efficacy should be optimised by limiting risks and costs and by incorporating patients' preferences. Factors influencing an individual's drug response in major depressive disorder may include a number of clinical variables (such as previous treatments, severity of illness, concomitant anxiety etc) as well demographics (for instance, age, weight, social support and family history). Our project, funded by the National Institute of Health Research, is aimed at developing and subsequently testing a precision medicine approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday clinical settings., Methods and Analysis: We will jointly synthesise data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as observational, real-world studies. We will summarise the highest quality and most up-to-date scientific evidence about comparative effectiveness and tolerability (adverse effects) of antidepressants for major depressive disorder, develop and externally validate prediction models to produce stratified treatment recommendations. Results from this analysis will subsequently inform a web-based platform and build a decision support tool combining the stratified recommendations with clinicians and patients' preferences, to adapt the tool, increase its' reliability and tailor treatment indications to the individual-patient level. We will then test whether use of the tool relative to treatment as usual in real-world clinical settings leads to enhanced treatment adherence and response, is acceptable to clinicians and patients, and is economically viable in the UK National Health Service., Discussion: This is a clinically oriented study, coordinated by an international team of experts, with important implications for patients treated in real-world setting. This project will form a test-case that, if effective, will be extended to non-pharmacological treatments (either face-to-face or internet-delivered), to other populations and disorders in psychiatry (for instance, children and adolescents, or schizophrenia and treatment-resistant depression) and to other fields of medicine., Competing Interests: Competing interests: No, there are no competing interests., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Background: In fixed-dose antidepressant trials, the lower range of the licensed dose achieves the optimal balance between efficacy and tolerability. Whether flexible upward titration while side-effects permit provides additional benefits is unknown., Methods: We did a systematic review of placebo-controlled randomized trials that examined selective serotonin reuptake inhibitors (SSRIs), venlafaxine or mirtazapine in the acute treatment of major depression. Our primary outcome was response, defined as 50% or greater reduction in depression severity. Secondary outcomes included drop-outs due to adverse effects and drop-outs for any reason. We conducted random-effects meta-analyses to calculate the ratios of odds ratios (RORs) between trials comparing the flexible dose titrating above the minimum licensed dose against placebo and those comparing the fixed minimum licensed dose against placebo., Results: We included 123 published and unpublished randomized controlled trials (29 420 participants). There was no evidence supporting efficacy of the flexible dosing over the fixed low dose of SSRIs (ROR 0.96, 95% CI: 0.73 to 1.25), venlafaxine (1.24, 0.96 to 1.60) or mirtazapine (0.77, 0.33 to 1.78). No important differences were noted for tolerability or for any subgroup analyses except the superior efficacy of venlafaxine flexible dosing between 75 and 150 mg over the fixed 75 mg (1.30, 1.02 to 1.65)., Conclusion: There was no evidence to support added value in terms of efficacy, tolerability or acceptability of flexibly titrating up the dosage over the minimum licensed dose of SSRIs or mirtazapine. For venlafaxine, increased efficacy can be expected by flexibly titrating up to 150 mg., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: We have recently compared all second-generation as well as selected first-generation antidepressants in terms of efficacy and acceptability in the acute treatment of major depression. Here we present a protocol for a network meta-analysis aimed at extending these results, updating the evidence base and comparing all second-generation as well as selected first-generation antidepressants in terms of specific adverse events and tolerability in the acute treatment of major depression in adults., Methods and Analysis: We will include all double-blind randomised controlled trials comparing one active drug with another or with placebo in the acute treatment major depression in adults. We will compare 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 include the total number of patients experiencing specific adverse events; experiencing serious adverse events; and experiencing at least one adverse event. Published and unpublished studies will be retrieved through relevant database searches, trial registries and websites; reference selection and data extraction will be completed by at least two independent reviewers. For each outcome we will undertake a network meta-analysis to synthesise all evidence. We will use local and global methods to evaluate consistency. We will perform all analyses in R. We will assess the quality of evidence contributing to network estimates with the Confidence in Network Meta-Analysis web application., Discussion: This work will provide an in- depth analysis and an insight into the specific adverse events of individual antidepressants., Ethics and Dissemination: This review does not require ethical approval., Prospero Registration Number: CRD42019128141., Competing Interests: Competing interests: SK reports grants from the Mental Health Okamoto Memorial Foundation, Pfizer Health Research Foundation and KDDI Foundation outside the submitted work. IH reports lecture fees from Mitsubishi-Tanabe and Yoshitomi. TAF reports personal fees from Meiji, Mitsubishi-Tanabe, MSD and Pfizer and a grant from Mitsubishi-Tanabe, outside the submitted work; TAF has a pending patent 2018-177688. All other authors report no conflict of interest, (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Depressive disorders are the most common, burdensome and costly mental disorders. Their treatments have developed through the past decades and we now have more than a dozen new generation antidepressants, while a series of guidelines have been published to provide recommendations over the years. However, there still may exist important gaps in this evidence synthesis and implementation process. Systematic reviews may not have been conducted in the most unbiased, informative and timely manners; guidelines may not have reflected the most up-to-date evidence; clinicians may not have changed their clinical decision-makings in accordance with the relevant evidence. The aim of this study is to examine the gaps between the ideally synthesised evidence, guideline recommendations and real-world clinical practices in the prescription of new generation antidepressants for major depression through the past three decades., Methods and Analysis: We will conduct cumulative network meta-analyses (cNMAs) based on the comprehensive systematic review which has identified published and unpublished head-to-head randomised controlled trials comparing the following antidepressants in the acute phase treatment of major depression: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. The primary outcomes will be the proportions of patients who responded (efficacy) and who withdrew from treatment for any reasons (acceptability). We will conduct a random effects cNMA to synthesise evidence and obtain a comprehensive ranking of all new generation antidepressants based on their surface under the cumulative ranking curves. We will identify series of international clinical practice guidelines for the treatment of major depression of adults and summarise their recommendations. We will estimate real-world prescription patterns of antidepressants in the nationally representative samples in USA in the Medical Expenditure Panel Survey. We will compare and evaluate the gaps between the rankings according to cNMAs conducted at 5-year intervals between 1990 and 2015, recommendations in guidelines published in the ensuing 5 years and actual practices thereafter., Ethics and Dissemination: This review does not require ethical approval. We will disseminate our findings through publications in peer-reviewed journals and presentations at conferences., Trial Registration Number: UMIN000031898., Competing Interests: Competing interests: TAF has received lecture fees from Janssen, Meiji, Mitsubishi-Tanabe, MSD and Pfizer. He has received research support from Mitsubishi-Tanabe and Mochida. ACi is supported by the NIHR Oxford Cognitive Health Clinical Research Facility, and was expert witness for Accord Healthcare for a patent issue about quetiapine extended release., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: There is debate in the literature as to whether inclusion of a placebo arm may alter characteristics of antidepressant trials. However, previous research has focused on response rates of various antidepressants on average only, ignoring potential differences among drugs or other aspects of trial findings. Little is known about the impact of a placebo arm on all-cause dropout and dropout due to adverse events., Methods: We carried out a systematic review of published and unpublished double-blind randomized controlled trials (RCTs) for the acute treatment of unipolar major depression (update: January 2016). The probability of being allocated to placebo (π) was the exposure of interest, and we examined its influence on responders (efficacy), all-cause dropouts (acceptability) and dropouts due to adverse events (tolerability), while accounting for differences in drugs, trials and patient characteristics in multivariate random effects meta-regression., Results: We included 421 studies (68 305 participants) comparing 16 antidepressants or placebo; π ranged from 20% to 50%. Response rate was lower [risk ratio (RR) 0.87; 95% confidence interval (CI) 0.83, 0.92] and all-cause dropout rate higher (RR 1.19; 95% CI 1.08, 1.31) for the same antidepressants in placebo-controlled trials compared with head-to-head trials. The probability of responding decreased by 3% (95% CI 2-5%) for every 10% increase in π, whereas the risk of all-cause dropout increased by 4% (95% CI 1-7%). Tolerability was unaffected by π. Response rate was inversely correlated with dropouts due to any cause (correlation coefficient -0.48; 95% CI -0.58, -0.36) and due to adverse events (-0.34; 95% CI -0.44, -0.23)., Conclusions: For the same antidepressant, response rate was on average smaller and dropouts higher when placebo was included; however, no association was found with dropouts due to adverse events. Decreased patient expectations, larger dropout rates and use of inappropriate statistical methods to impute missing data may explain this phenomenon. The findings call for caution in the integration of randomized evidence involving placebo arms.

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., Funding: National Institute for Health Research Oxford Health Biomedical Research Centre and the Japan Society for the Promotion of Science., (Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Previous studies have reported that effect sizes of antidepressants were larger in two-armed than in three-armed or more-armed (multiarmed) randomized trials, where the probability of being allocated to placebo is lower. However, these studies have not taken into account the publication bias, differences among antidepressants, or covariance in multiarmed studies, or examined sponsorship bias. We searched published and unpublished randomized-controlled trials that compared placebo with 21 antidepressants for the acute treatment of major depression in adults. We calculated the ratio of odds ratios (ROR) of drug response over placebo in two-armed versus multiarmed trials for each antidepressant, and then synthesized RORs across all the included antidepressants using the multivariate meta-analysis. A random-effects model was used throughout. Two hundred and fifty-eight trials (66 two-armed and 192 multiarmed trials; 80 454 patients; 43.0% with unpublished data) were included in the present analyses. The pooled ROR for response of two-armed trials over multiarmed trials was 1.09 (95% confidence interval: 0.96-1.24). The ROR did not materially change between types of antidepressants, publication year, or sponsorship. The differences between two-armed versus multiarmed studies were much smaller than were suggested in previous studies and were not significant.

It had long been believed that placebo response rates in antidepressant trials have been increasing and that they were responsible for rising numbers of so-called failed antidepressant trials. Two recent systematic reviews examined this issue and reached completely opposite findings. Furukawa and colleagues in a paper published in 2016 found that the placebo response rates are stable since 1991 and the apparent increase up to 2000 was confounded by changes in trial design features. By contrast, Khan and colleagues more recently concluded that placebo response rates had grown steadily in the past 30 years. The two reviews differed in the datasets they used, definitions of placebo response and statistical analyses. In this perspective article, we examined if such differences were responsible for the two reviews' contrasting conclusions. Our reanalyses confirmed our previous results. We found that in any dataset and for any placebo response definition, there was no increase in placebo response over the years when the analysis was adjusted for the confounders related to study design features or when it was limited to studies published after 1990s. We conclude that placebo response in antidepressant trials has remained stable for the past 25 years, during which time the large majority of the studies have come to share similar design features., Competing Interests: Competing interests: TAF has received lecture fees from Eli Lilly, Janssen, Meiji, MSD, Pfizer and Tanabe-Mitsubishi and consultancy fees from Takeda Science Foundation. He has received royalties from Igaku-Shoin and Nihon Bunka Kagaku-sha publishers. He has received grant or research support from Mochida and Tanabe-Mitsubishi. AC was expert witness for Accord Healthcare for a patent issue about quetiapine extended release. SL has received honoraria for lectures from Eli Lilly, Lundbeck (Institute), Pfizer, Janssen, BMS, Johnson and Johnson, Otsuka, Roche, SanofiAventis, ICON, Abbvie, AOP Orphan, Servier; for consulting/advisory boards from Roche, Janssen, Lundbeck, Eli Lilly, Otsuka, TEVA; for the preparation of educational material and publications from Lundbeck Institute and Roche. Eli Lilly has provided medication for a clinical trial led by SL as principal investigator. All the other authors declare no competing interests., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background: Persistent depressive disorder is prevalent, disabling, and often difficult to treat. The cognitive-behavioral analysis system of psychotherapy (CBASP) is the only psychotherapy specifically developed for its treatment. However, we do not know which of CBASP, antidepressant pharmacotherapy, or their combination is the most efficacious and for which types of patients. This study aims to present personalized prediction models to facilitate shared decision-making in treatment choices to match patients' characteristics and preferences based on individual participant data network metaregression., Methods: We conducted a comprehensive search for randomized controlled trials comparing any two of CBASP, pharmacotherapy, or their combination and sought individual participant data from identified trials. The primary outcomes were reduction in depressive symptom severity for efficacy and dropouts due to any reason for treatment acceptability., Results: All 3 identified studies (1,036 participants) were included in the present analyses. On average, the combination therapy showed significant superiority over both monotherapies in terms of efficacy and acceptability, while the latter 2 treatments showed essentially similar results. Baseline depression, anxiety, prior pharmacotherapy, age, and depression subtypes moderated their relative efficacy, which indicated that for certain subgroups of patients either drug therapy or CBASP alone was a recommendable treatment option that is less costly, may have fewer adverse effects and match an individual patient's preferences. An interactive web app (https://kokoro.med.kyoto-u.ac.jp/CBASP/prediction/) shows the predicted disease course for all possible combinations of patient characteristics., Conclusions: Individual participant data network metaregression enables treatment recommendations based on individual patient characteristics., (© 2018 S. Karger AG, Basel.)

Background: Previous studies have shown that placebo response rates in antidepressant trials have been increasing since the 1970s. However, these studies have been based on outdated or limited datasets and have used inappropriate statistical methods. We did a systematic review of placebo-controlled randomised controlled trials of antidepressants to examine associations between placebo-response rates and study and patient characteristics., Methods: In this systematic review, we searched for published and unpublished double-blind randomised placebo-controlled trials of first-generation and second-generation antidepressants for acute treatment of major depression in adults (update: Jan 8, 2016). The log-transformed proportions of placebo response, defined as 50% or greater reduction in depression severity score from baseline, were meta-analytically synthesised for each year. We then looked for a structural break point in the secular changes in these characteristics through the years and examined the influence of the study year and other trial and patient characteristics on the response rates through meta-regression., Findings: We identified 252 placebo-controlled trials (26 324 patients on placebo) done between 1978 and 2015. There was a structural break in 1991, and since then, the average placebo response rates in antidepressant trials have remained constant in the range between 35% and 40% (relative risk [RR] 1·00, 95% CI 0·97-1·03, p=0·99, for every 5-year increase). The length of the study and the number of study centres were significant factors (RR 1·03, 95% CI 1·01-1·05 for 1 more week in trial length; 1·32, 1·11-1·57 for multicentre vs single-centre trials)., Interpretation: Contrary to the widely held belief, the average placebo response rates in antidepressant trials have been stable for more than 25 years. This new evidence should have an effect on the interpretation of the scientific literature and the future of psychopharmacology, both from a clinical and methodological point of view., Funding: Japan Society for Promotion of Science, Great Britain Sasakawa Foundation., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: In a meta-analysis of trials with missing outcome data, a parameter known as informative missing odds ratio (IMOR) can be used to quantify the relationship between informative missingness and a binary outcome. IMORs also account for the increased uncertainty due to missingness in the meta-analysis results., Purpose: To extend the idea of IMOR into a network meta-analysis (NMA) setting in order to explore the impact of missing outcome data on the inferences about the relative effectiveness of several competing treatments in psychiatric trials., Methods: IMORs were estimated in two datasets comparing anti-manic treatments and antidepressants. The outcome was response to treatments. In the original meta-analyses, missing participants were assumed to have failed regardless the treatment they were allocated to. To evaluate the robustness of this assumption in each dataset, several imputations of the missing outcomes were studied by an IMOR parameter in the NMA model. By comparing the odds ratios for efficacy under the initial analysis and under several assumptions about the missingness, we assessed the consistency of the conclusions. The missing data mechanism was studied by comparing the prior with the posterior IMOR distribution. Models were fitted using Markov chain Monte Carlo (MCMC) in WinBUGS., Results: In both datasets, the relative effectiveness of the treatments seems to be affected only by the two extreme imputation scenarios of worst- and best-case analyses. Moreover, heterogeneity increases in both datasets under these two extreme scenarios. Overall, there is a non-significant change on the ranking of the anti-manic and antidepressant treatments. The posterior and prior IMOR distributions are very similar showing that the data do not provide any information about the true outcome in missing participants. There is a very weak indication that missing participants tend to fail in placebo and paroxetine, while the opposite occurs for sertraline, fluoxetine, and fluvoxamine., Limitations: Investigation of informative missingness was limited two classes of treatments and for dichotomous outcome measures. The proportion of missing outcomes was very low overall, and hence, the power of detecting any differences in effectiveness estimated under the various imputation methods is small., Conclusions: Sensitivity analysis to account for missing outcome data and their uncertainty in the NMA can be undertaken by extending the idea of IMOR. In two case examples, we found no differences between the various models due to low missing data rate. In line with previous observations, data carry little information about the reason of missingness.

Background: Conventional meta-analyses have shown inconsistent results for efficacy of second-generation antidepressants. We therefore did a multiple-treatments meta-analysis, which accounts for both direct and indirect comparisons, to assess the effects of 12 new-generation antidepressants on major depression., Methods: We systematically reviewed 117 randomised controlled trials (25 928 participants) from 1991 up to Nov 30, 2007, which compared any of the following antidepressants at therapeutic dose range for the acute treatment of unipolar major depression in adults: bupropion, citalopram, duloxetine, escitalopram, fluoxetine, fluvoxamine, milnacipran, mirtazapine, paroxetine, reboxetine, sertraline, and venlafaxine. The main outcomes were the proportion of patients who responded to or dropped out of the allocated treatment. Analysis was done on an intention-to-treat basis., Findings: Mirtazapine, escitalopram, venlafaxine, and sertraline were significantly more efficacious than duloxetine (odds ratios [OR] 1.39, 1.33, 1.30 and 1.27, respectively), fluoxetine (1.37, 1.32, 1.28, and 1.25, respectively), fluvoxamine (1.41, 1.35, 1.30, and 1.27, respectively), paroxetine (1.35, 1.30, 1.27, and 1.22, respectively), and reboxetine (2.03, 1.95, 1.89, and 1.85, respectively). Reboxetine was significantly less efficacious than all the other antidepressants tested. Escitalopram and sertraline showed the best profile of acceptability, leading to significantly fewer discontinuations than did duloxetine, fluvoxamine, paroxetine, reboxetine, and venlafaxine., Interpretation: Clinically important differences exist between commonly prescribed antidepressants for both efficacy and acceptability in favour of escitalopram and sertraline. Sertraline might be the best choice when starting treatment for moderate to severe major depression in adults because it has the most favourable balance between benefits, acceptability, and acquisition cost.