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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 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. This article is protected by copyright. All rights reserved.

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will work for whom and in what contexts. To intervene early in the process and deliver optimal care to patients, we need to understand the underlying mechanisms of mental health conditions, develop safe and effective interventions that target these mechanisms, and improve our capabilities in timely diagnosis and reliable prediction of symptom trajectories. Better synthesis of existing evidence is one way to reduce waste and improve efficiency in research towards these ends. Living systematic reviews produce rigorous, up-to-date and informative evidence summaries that are particularly important where research is emerging rapidly, current evidence is uncertain and new findings might change policy or practice. Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) aims to tackle the challenges of mental health science research by cataloguing and evaluating the full spectrum of relevant scientific research including both human and preclinical studies. GALENOS will also allow the mental health community-including patients, carers, clinicians, researchers and funders-to better identify the research questions that most urgently need to be answered. By creating open-access datasets and outputs in a state-of-the-art online resource, GALENOS will help identify promising signals early in the research process. This will accelerate translation from discovery science into effective new interventions for anxiety, depression and psychosis, ready to be translated in clinical practice across the world.

IMPORTANCE Current evidence remains ambiguous regarding whether biologics should be added to conventional treatment of rheumatoid arthritis for specific patients, which may cause potential overuse or treatment delay. OBJECTIVES To estimate the benefit of adding biologics to conventional antirheumatic drugs for the treatment of rheumatoid arthritis given baseline characteristics. DATA SOURCES Cochrane CENTRAL, Scopus, MEDLINE, and the World Health Organization International Clinical Trials Registry Platform were searched for articles published from database inception to March 2, 2022. STUDY SELECTION Randomized clinical trials comparing certolizumab plus conventional antirheumatic drugs with placebo plus conventional drugs were selected. DATA EXTRACTION AND SYNTHESIS Individual participant data of the prespecified outcomes and covariates were acquired from the Vivli database. A 2-stage model was fitted to estimate patient-specific relative outcomes of adding certolizumab vs conventional drugs only. Stage 1 was a penalized logistic regression model to estimate the baseline expected probability of the outcome regardless of treatment using baseline characteristics. Stage 2 was a bayesian individual participant data meta-regression model to estimate the relative outcomes for a particular baseline expected probability. Patient-specific results were displayed interactively on an application based on a 2-stage model. MAIN OUTCOMES AND MEASURES The primary outcome was low disease activity or remission at 3 months, defined by 3 disease activity indexes (ie, Disease Activity Score based on the evaluation of 28 joints, Clinical Disease Activity Index, or Simplified Disease Activity Index). RESULTS Individual participant data were obtained from 3790 patients (2996 female [79.1%] and 794 male [20.9%]; mean [SD] age, 52.7 [12.3] years) from 5 large randomized clinical trials for moderate to high activity rheumatoid arthritis with usable data for 22 prespecified baseline covariates. Overall, adding certolizumab was associated with a higher probability of reaching low disease activity. The odds ratio for patients with an average baseline expected probability of the outcome was 6.31 (95% credible interval, 2.22-15.25). However, the benefits differed in patients with different baseline characteristics. For example, the estimated risk difference was smaller than 10% for patients with either low or high baseline expected probability. CONCLUSIONS AND RELEVANCE In this individual participant data meta-analysis, adding certolizumab was associated with more effectiveness for rheumatoid arthritis in general. However, the benefit was uncertain for patients with low or high baseline expected probability, for whom other evaluations were necessary. The interactive application displaying individual estimates may help with treatment selection.

BACKGROUND Schizophrenia is a common, severe, and usually chronic disorder. Maintenance treatment with antipsychotic drugs can prevent relapse but also causes side-effects. We aimed to compare the efficacy and tolerability of antipsychotics as maintenance treatment for non-treatment resistant patients with schizophrenia. METHODS In this systematic review and network meta-analysis, we searched, without language restrictions, the Cochrane Schizophrenia Group's specialised register between database inception and April 27, 2020, PubMed from April 1, 2020, to Jan 15, 2021, and the lists of included studies from related systematic reviews. We included randomised controlled trials (RCTs; ���12 weeks of follow-up) that recruited adult participants with schizophrenia or schizoaffective disorder with stable symptoms who were treated with antipsychotics (monotherapy; oral or long-acting injectable) or placebo. We excluded RCTs of participants with specific comorbidities or treatment resistance. In duplicate, two authors independently selected eligible RCTs and extracted aggregate data. The primary outcome was the number of participants who relapsed and was analysed by random-effects, Bayesian network meta-analyses. The study was registered on PROSPERO, CRD42016049022. FINDINGS We identified 4157 references through our search, from which 501 references on 127 RCTs of 32 antipsychotics (comprising 18���152 participants) were included. 100 studies including 16���812 participants and 30 antipsychotics contributed to our network meta-analysis of the primary outcome. All antipsychotics had risk ratios (RRs) less than 1��00 when compared with placebo for relapse prevention and almost all had 95% credible intervals (CrIs) excluding no effect. RRs ranged from 0��20 (95% CrI 0��05-0��41) for paliperidone oral to 0��65 (0��16-1��14) for cariprazine oral (moderate-to-low confidence in estimates). Generally, we interpret that there was no clear evidence for the superiority of specific antipsychotics in terms of relapse prevention because most comparisons between antipsychotics included a probability of no difference. INTERPRETATION As we found no clear differences between antipsychotics for relapse prevention, we conclude that the choice of antipsychotic for maintenance treatment should be guided mainly by their tolerability. FUNDING The German Ministry of Education and Research and Oxford Health Biomedical Research Centre.

Most acute phase antipsychotic drug trials in schizophrenia last only a few weeks, but patients must usually take these drugs much longer. We examined the long-term efficacy of antipsychotic drugs in acutely ill patients using network meta-analysis. We searched the Cochrane Schizophrenia Group register up to March 6, 2022 for randomized, blinded trials of at least 6-month duration on all second-generation and 18 first-generation antipsychotics. The primary outcome was change in overall symptoms of schizophrenia; secondary outcomes were all-cause discontinuation; change in positive, negative and depressive symptoms; quality of life, social functioning, weight gain, antiparkinson medication use, akathisia, serum prolactin level, QTc prolongation, and sedation. Confidence in the results was assessed by the CINeMA (Confidence in Network Meta-Analysis) framework. We included 45 studies with 11,238 participants. In terms of overall symptoms, olanzapine was on average more efficacious than ziprasidone (standardized mean difference, SMD=0.37, 95% CI: 0.26-0.49), asenapine (SMD=0.33, 95% CI: 0.21-0.45), iloperidone (SMD=0.32, 95% CI: 0.15-0.49), paliperidone (SMD=0.28, 95% CI: 0.11-0.44), haloperidol (SMD=0.27, 95% CI: 0.14-0.39), quetiapine (SMD=0.25, 95% CI: 0.12-0.38), aripiprazole (SMD=0.16, 95% CI: 0.04-0.28) and risperidone (SMD=0.12, 95% CI: 0.03-0.21). The 95% CIs for olanzapine versus aripiprazole and risperidone included the possibility of trivial effects. The differences between olanzapine and lurasidone, amisulpride, perphenazine, clozapine and zotepine were either small or uncertain. These results were robust in sensitivity analyses and in line with other efficacy outcomes and all-cause discontinuation. Concerning weight gain, the impact of olanzapine was higher than all other antipsychotics, with a mean difference ranging from -4.58 kg (95% CI: -5.33 to -3.83) compared to ziprasidone to -2.30 kg (95% CI: -3.35 to -1.25) compared to amisulpride. Our data suggest that olanzapine is more efficacious than a number of other antipsychotic drugs in the longer term, but its efficacy must be weighed against its side effect profile.

BACKGROUND The effect of early as compared with later initiation of direct oral anticoagulants (DOACs) in persons with atrial fibrillation who have had an acute ischemic stroke is unclear. METHODS We performed an investigator-initiated, open-label trial at 103 sites in 15 countries. Participants were randomly assigned in a 1:1 ratio to early anticoagulation (within 48 hours after a minor or moderate stroke or on day 6 or 7 after a major stroke) or later anticoagulation (day 3 or 4 after a minor stroke, day 6 or 7 after a moderate stroke, or day 12, 13, or 14 after a major stroke). Assessors were unaware of the trial-group assignments. The primary outcome was a composite of recurrent ischemic stroke, systemic embolism, major extracranial bleeding, symptomatic intracranial hemorrhage, or vascular death within 30 days after randomization. Secondary outcomes included the components of the composite primary outcome at 30 and 90 days. RESULTS Of 2013 participants (37% with minor stroke, 40% with moderate stroke, and 23% with major stroke), 1006 were assigned to early anticoagulation and 1007 to later anticoagulation. A primary-outcome event occurred in 29 participants (2.9%) in the early-treatment group and 41 participants (4.1%) in the later-treatment group (risk difference, -1.18 percentage points; 95% confidence interval [CI], -2.84 to 0.47) by 30 days. Recurrent ischemic stroke occurred in 14 participants (1.4%) in the early-treatment group and 25 participants (2.5%) in the later-treatment group (odds ratio, 0.57; 95% CI, 0.29 to 1.07) by 30 days and in 18 participants (1.9%) and 30 participants (3.1%), respectively, by 90 days (odds ratio, 0.60; 95% CI, 0.33 to 1.06). Symptomatic intracranial hemorrhage occurred in 2 participants (0.2%) in both groups by 30 days. CONCLUSIONS In this trial, the incidence of recurrent ischemic stroke, systemic embolism, major extracranial bleeding, symptomatic intracranial hemorrhage, or vascular death at 30 days was estimated to range from 2.8 percentage points lower to 0.5 percentage points higher (based on the 95% confidence interval) with early than with later use of DOACs. (Funded by the Swiss National Science Foundation and others; ELAN ClinicalTrials.gov number, NCT03148457.).

OBJECTIVE When developing prediction models, researchers commonly employ a single model which uses all the available data (end-to-end approach). Alternatively, a similarity-based approach has been previously proposed, in which patients with similar clinical characteristics are first grouped into clusters, then prediction models are developed within each cluster. The potential advantage of the similarity-based approach is that it may better address heterogeneity in patient characteristics. However, it remains unclear whether it improves the overall predictive performance. We illustrate the similarity-based approach using data from people with depression and empirically compare its performance with the end-to-end approach. METHODS We used primary care data collected in general practices in the UK. Using 31 predefined baseline variables, we aimed to predict the severity of depressive symptoms, measured by Patient Health Questionnaire-9, 60 days after initiation of antidepressant treatment. Following the similarity-based approach, we used k-means to cluster patients based on their baseline characteristics. We derived the optimal number of clusters using the Silhouette coefficient. We used ridge regression to build prediction models in both approaches. To compare the models' performance, we calculated the mean absolute error (MAE) and the coefficient of determination (R2) using bootstrapping. RESULTS We analysed data from 16 384 patients. The end-to-end approach resulted in an MAE of 4.64 and R2 of 0.20. The best-performing similarity-based model was for four clusters, with MAE of 4.65 and R2 of 0.19. CONCLUSIONS The end-to-end and the similarity-based model yielded comparable performance. Due to its simplicity, the end-to-end approach can be favoured when using demographic and clinical data to build prediction models on pharmacological treatments for depression.

BackgroundAripiprazole augmentation is proven effective for antidepressant-refractory depression, but its licensed dose range is wide and optimal dosage remains unclear.AimsTo find the optimal dosage of aripiprazole augmentation.MethodMultiple electronic databases were searched (from inception to 16 February 2021) to identify all assessor-masked randomised controlled trials evaluating aripiprazole augmentation therapy in adults (≥18 years old, both genders) with major depressive disorder showing inadequate response to at least one antidepressant treatment. A random-effects, one-stage dose–effect meta-analysis with restricted cubic splines was conducted. Outcomes were efficacy (treatment response: ≥50% reduction in depression severity), tolerability (drop-out due to adverse effects) and acceptability (drop-out for any reason) after 8 weeks of treatment (range 4–12 weeks).ResultsTen studies met the inclusion criteria. All were individually randomised, placebo-controlled, multi-centre, parallel studies including 2625 participants in total. The maximum target dose–efficacy curve showed an increase up to doses between 2 mg (odds ratio OR = 1.46, 95% CI 1.15–1.85) and 5 mg (OR = 1.93, 95% CI 1.33–2.81), and then a non-increasing trend through the higher licensed doses up to 20 mg (OR = 1.90, 95% CI 1.52–2.37). Tolerability showed a similar trend with greater uncertainty. Acceptability showed no significant difference through the examined dose range. Certainty of evidence was low to moderate.ConclusionsLow-dose aripiprazole as augmentation treatment might achieve the optimal balance between efficacy, tolerability and acceptability in the acute treatment of antidepressant-refractory depression. However, the small number of included studies and the overall moderate to high risk of bias seriously compromise the reliability of the results. Further research is required to investigate the benefits of low versus high dose.

Background Selective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analyses and can affect clinical decision-making. A rigorous method to evaluate the impact of this bias on the results of network meta-analyses of interventions is lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN). Methods ROB-MEN first evaluates the risk of bias due to missing evidence for each of the possible pairwise comparison that can be made between the interventions in the network. This step considers possible bias due to the presence of studies with unavailable results (within-study assessment of bias) and the potential for unpublished studies (across-study assessment of bias). The second step combines the judgements about the risk of bias due to missing evidence in pairwise comparisons with (i) the contribution of direct comparisons to the network meta-analysis estimates, (ii) possible small-study effects evaluated by network meta-regression, and (iii) any bias from unobserved comparisons. Then, a level of “low risk”, “some concerns”, or “high risk” for the bias due to missing evidence is assigned to each estimate, which is our tool’s final output. Results We describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder. Conclusions ROB-MEN is the first tool for evaluating the risk of bias due to missing evidence in network meta-analysis and applies to networks of all sizes and geometry. The use of ROB-MEN is facilitated by an R Shiny web application that produces the Pairwise Comparisons and ROB-MEN Table and is incorporated in the reporting bias domain of the CINeMA framework and software.

BACKGROUND Many psychosocial and psychological interventions are used in patients with schizophrenia, but their comparative efficacy in the prevention of relapse is not known. We aimed to evaluate the efficacy, acceptability, and tolerability of psychosocial and psychological interventions for relapse prevention in schizophrenia. METHODS To conduct this systematic review and network meta-analysis we searched for published and unpublished randomised controlled trials that investigated psychosocial or psychological interventions aimed at preventing relapse in patients with schizophrenia. We searched EMBASE, MEDLINE, PsycINFO, BIOSIS, Cochrane Library, WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov up to Jan 20, 2020, and searched PubMed up to April 14, 2020. We included open and masked studies done in adults with schizophrenia or related disorders. We excluded studies in which all patients were acutely ill, had a concomitant medical or psychiatric disorder, or were prodromal or "at risk of psychosis". Study selection and data extraction were done by two reviewers independently based on published and unpublished reports, and by contacting study authors. Data were extracted about efficacy, tolerability, and acceptability of the interventions; potential effect moderators; and study quality and characteristics. The primary outcome was relapse measured with operationalised criteria or psychiatric hospital admissions. We did random-effects network meta-analysis to calculate odds ratios (ORs) or standardised mean differences (SMDs) with 95% CIs. The study protocol was registered with PROSPERO, CRD42019147884. FINDINGS We identified 27 765 studies through the database search and 330 through references of previous reviews and studies. We screened 28 000 records after duplicates were removed. 24 406 records were excluded by title and abstract screening and 3594 full-text articles were assessed for eligibility. 3350 articles were then excluded for a variety of reasons, and 244 full-text articles corresponding to 85 studies were included in the qualitative synthesis. Of these, 72 studies with 10 364 participants (3939 females and 5716 males with sex indicated) were included in the network meta-analysis. The randomised controlled trials included compared 20 psychological interventions given mainly as add-on to antipsychotics. Ethnicity data were not available. Family interventions (OR 0·35, 95% CI 0·24-0·52), relapse prevention programmes (OR 0·33, 0·14-0·79), cognitive behavioural therapy (OR 0·45, 0·27-0·75), family psychoeducation (OR 0·56, 0·39-0·82), integrated interventions (OR 0·62, 0·44-0·87), and patient psychoeducation (OR 0·63, 0·42-0·94) reduced relapse more than treatment as usual at 1 year. The confidence in the estimates ranged from moderate to very low. We found no indication of publication bias. INTERPRETATION We found robust benefits in reducing the risk of relapse for family interventions, family psychoeducation, and cognitive behavioral therapy. These treatments should be the first psychosocial interventions to be considered in the long-term treatment for patients with schizophrenia. FUNDING German Ministry for Education and Research.

IntroductionGuidelines recommend clozapine for treatment-resistant schizophrenia. However, meta-analysis of aggregate data (AD) did not demonstrate higher efficacy of clozapine compared with other second-generation antipsychotics but found substantial heterogeneity between trials and variation between participants in treatment effects. Therefore, we will conduct an individual participant data (IPD) meta-analysis to estimate the efficacy of clozapine compared with other second-generation antipsychotics while accounting for potentially important effect modifiers.Methods and analysisIn a systematic review, two reviewers will independently search Cochrane Schizophrenia Group’s trial register (without restrictions in date, language or state of publication) and related reviews. We will include randomised controlled trials (RCTs) in participants with treatment-resistant schizophrenia comparing clozapine with other second-generation antipsychotics for at least 6 weeks. We will apply no restrictions in age, gender, origin, ethnicity or setting, but exclude open-label studies, studies from China, experimental studies and phase II of cross-over trials. IPD will be requested from trial authors and cross-check against published results. AD will be extracted in duplicate. Risk of bias will be assessed using Cochrane’s Risk of Bias 2 tool.The primary outcome will be overall symptoms of schizophrenia.We will synthesise results using random-effects meta-analysis and meta-regression methods in a 3-level Bayesian model. The model combines IPD with AD when IPD is not available for all studies, and include participant, intervention and study design characteristics as potential effect modifiers. The effect size measures will be mean difference (or standardised mean difference when different scales were used). Confidence in the evidence will be assessed using GRADE.Ethics and disseminationThis project has been approved by the ethics commission of the Technical University of Munich (#612/21 S-NP). The results will be published open-access in a peer-review journal and a plain-language version of the results will be disseminated.If we need to amend this protocol, we will describe the change and give the rationale in a specific section in the resulting publication ‘Changes with respect to the protocol’.Systematic review registrationPROSPERO (#CRD42021254986)

Background: In randomized controlled trials (RCTs), the power is often 'reverse engineered' based on the number of participants that can realistically be achieved. An attractive alternative is planning a new trial conditional on the available evidence; a design of particular interest in RCTs that use a sham control arm (sham-RCTs). Methods: We explore the design of sham-RCTs, the role of sequential meta-analysis and conditional planning in a systematic review of renal sympathetic denervation for patients with arterial hypertension. The main efficacy endpoint was mean change in 24-hour systolic blood pressure. We performed sequential meta-analysis to identify the time point where the null hypothesis would be rejected in a prospective scenario. Evidence-based conditional sample size calculations were performed based on fixed-effect meta-analysis. Results: In total, six sham-RCTs (981 participants) were identified. The first RCT was considerably larger (535 participants) than those subsequently published (median sample size of 80). All trial sample sizes were calculated assuming an unrealistically large intervention effect which resulted in low power when each study is considered as a stand-alone experiment. Sequential meta-analysis provided firm evidence against the null hypothesis with the synthesis of the first four trials (755 patients, cumulative mean difference -2.75 (95%CI -4.93 to -0.58) favoring the active intervention)). Conditional planning resulted in much larger sample sizes compared to those in the original trials, due to overoptimistic expected effects made by the investigators in individual trials, and potentially a time-effect association. Conclusions: Sequential meta-analysis of sham-RCTs can reach conclusive findings earlier and hence avoid exposing patients to sham-related risks. Conditional planning of new sham-RCTs poses important challenges as many surgical/minimally invasive procedures improve over time, the intervention effect is expected to increase in new studies and this violates the underlying assumptions. Unless this is accounted for, conditional planning will not improve the design of sham-RCTs.

BACKGROUND Prevalence measures the occurrence of any health condition, exposure or other factors related to health. The experience of COVID-19, a new disease caused by SARS-CoV-2, has highlighted the importance of prevalence studies, for which issues of reporting and methodology have traditionally been neglected. OBJECTIVE This communication highlights key issues about risks of bias in the design and conduct of prevalence studies and in reporting them, using examples about SARS-CoV-2 and COVID-19. SUMMARY The two main domains of bias in prevalence studies are those related to the study population (selection bias) and the condition or risk factor being assessed (information bias). Sources of selection bias should be considered both at the time of the invitation to take part in a study and when assessing who participates and provides valid data (respondents and non-respondents). Information bias appears when there are systematic errors affecting the accuracy and reproducibility of the measurement of the condition or risk factor. Types of information bias include misclassification, observer and recall bias. When reporting prevalence studies, clear descriptions of the target population, study population, study setting and context, and clear definitions of the condition or risk factor and its measurement are essential. Without clear reporting, the risks of bias cannot be assessed properly. Bias in the findings of prevalence studies can, however, impact decision-making and the spread of disease. The concepts discussed here can be applied to the assessment of prevalence for many other conditions. CONCLUSIONS Efforts to strengthen methodological research and improve assessment of the risk of bias and the quality of reporting of studies of prevalence in all fields of research should continue beyond this pandemic.

Background The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension statement for network meta-analysis (NMA) published in 2015 promotes comprehensive reporting in published systematic reviews with NMA. PRISMA-NMA includes 32 items: 27 core items as indicated in the 2009 PRISMA Statement and five items specific to the reporting of NMAs. Although NMA reporting is improving, it is unclear whether PRISMA-NMA has accelerated this improvement. We aimed to investigate the impact of PRISMA-NMA and highlight key items that require attention and improvement. Methods We updated our previous collection of NMAs with articles published between April 2015 and July 2018. We assessed the completeness of reporting for each NMA, including main manuscript and online supplements, using the PRISMA-NMA checklist. The PRISMA-NMA checklist originally includes 32 total items (i.e. a 32-point scale original PRISMA-NMA score). We also prepared a modified version of the PRISMA-NMA checklist with 49 items to evaluate separately at a more granular level all multiple-content items (i.e. a 49-point scale modified PRISMA-NMA score). We compared average reporting scores of articles published until and after 2015. Results In the 1144 included NMAs the mean modified PRISMA-NMA score was 32.1 (95% CI 31.8–32.4) of a possible 49-excellence-score. For 1-year increase, the mean modified score increased by 0.96 (95% CI 0.32 to 1.59) for 389 NMAs published until 2015 and by 0.53 (95% CI 0.02 to 1.04) for 755 NMAs published after 2015. The mean modified PRISMA-NMA score for NMAs published after 2015 was higher by 0.81 (95% CI 0.23 to 1.39) compared to before 2015 when adjusting for journal impact factor, type of review, funding, and treatment category. Description of summary effect sizes to be used, presentation of individual study data, sources of funding for the systematic review, and role of funders dropped in frequency after 2015 by 6–16%. Conclusions NMAs published after 2015 more frequently reported the five items associated with NMA compared to those published until 2015. However, improvement in reporting after 2015 is compatible with that observed on a yearly basis until 2015, and hence, it could not be attributed solely to the publication of the PRISMA-NMA.

Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision-making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta-analysis framework. We propose a two-stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta-analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta-regression model. We apply the approach to a network meta-analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing-remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high-risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low-risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.

Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: The trade-off between comparative effectiveness and reproductive morbidity of different treatment methods for cervical intraepithelial neoplasia (CIN) remains unclear. We aimed to determine the risks of treatment failure and preterm birth associated with various treatment techniques. Methods: In this systematic review and network meta-analysis, we searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials database for randomised and non-randomised studies reporting on oncological or reproductive outcomes after CIN treatments from database inception until March 9, 2022, without language restrictions. We included studies of women with CIN, glandular intraepithelial neoplasia, or stage IA1 cervical cancer treated with excision (cold knife conisation [CKC], laser conisation, and large loop excision of the transformation zone [LLETZ]) or ablation (radical diathermy, laser ablation, cold coagulation, and cryotherapy). We excluded women treated with hysterectomy. The primary outcomes were any treatment failure (defined as any abnormal histology or cytology) and preterm birth (

Objectives Network meta-analysis (NMA) may produce more precise estimates of treatment effects than pairwise meta-analysis. We examined the relative contribution of network paths of different lengths to estimates of treatment effects. Study Design and Setting We analyzed 213 published NMAs. We categorized network shapes according to the presence or absence of at least one closed loop (nonstar or star network) and derived the graph density, radius, and diameter. We identified paths of different lengths and calculated their percentage contribution to each NMA effect estimate, based on their contribution matrix. Results Among the 213 NMAs included in analyses, 33% of the information came from paths of length 1 (direct evidence), 47% from paths of length 2 (indirect paths with one intermediate treatment) and 20% from paths of length 3. The contribution of paths of different lengths depended on the size of networks, presence of closed loops, and graph radius, density, and diameter. Longer paths contribute more as the number of treatments and loops and the graph radius and diameter increase. Conclusion The contribution of different paths depends on the size and structure of networks, with important implications for assessing the risk of bias and confidence in NMA results.

Rationale: Direct oral anticoagulants (DOAC) are highly effective in preventing ischaemic strokes in people with atrial fibrillation (AF). However, it is unclear how soon they should be started after acute ischaemic stroke (AIS). Early initiation may reduce early risk of recurrence but might increase the risk of haemorrhagic complications. Aim: To estimate the safety and efficacy of early initiation of DOACs compared to late guideline-based initiation in people with AIS related to AF. Methods and design: An international, multicentre, randomised (1:1) controlled, two-arm, open, assessor-blinded trial is being conducted. Early treatment is defined as DOAC initiation within 48 h of a minor or moderate stroke, or at day 6–7 following major stroke. Late treatment is defined as DOAC initiation after day 3–4 following minor stroke, after day 6–7 following moderate stroke and after day 12–14 following major stroke. Severity of stroke is defined according to imaging assessment of infarct size. Sample size: ELAN will randomise 2000 participants 1:1 to early versus late initiation of DOACs. This assumes a risk difference of 0.5% favouring the early arm, allowing an upper limit of the 95% confidence interval up to 1.5% based on the Miettinen & Nurminen formula. Outcomes: The primary outcome is a composite of symptomatic intracranial haemorrhage, major extracranial bleeding, recurrent ischaemic stroke, systemic embolism or vascular death at 30 ± 3 days after randomisation. Secondary outcomes include the individual components of the primary outcome at 30 ± 3 and 90 ± 7 days and functional status at 90 ± 7 days. Discussion: ELAN will estimate whether there is a clinically important difference in safety and efficacy outcomes following early anticoagulation with a DOAC compared to late guideline-based treatment in neuroimaging-selected people with an AIS due to AF.

BACKGROUNDDebate about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues. The amount of evidence is increasing and study designs have changed over time. We updated a living systematic review to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) What is the infectiousness of asymptomatic and presymptomatic, compared with symptomatic, SARS-CoV-2 infection? (3) What proportion of SARS-CoV-2 transmission in a population is accounted for by people who are asymptomatic or presymptomatic?METHODS AND FINDINGSThe protocol was first published on 1 April 2020 and last updated on 18 June 2021. We searched PubMed, Embase, bioRxiv and medRxiv, aggregated in a database of SARS-CoV-2 literature, most recently on 6 July 2021. Studies of people with PCR-diagnosed SARS-CoV-2, which documented symptom status at the beginning and end of follow-up, or mathematical modelling studies were included. Studies restricted to people already diagnosed, of single individuals or families, or without sufficient follow-up were excluded. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with a bespoke checklist and modelling studies with a published checklist. All data syntheses were done using random effects models. Review question (1): We included 130 studies.Heterogeneity was high so we did not estimate a mean proportion of asymptomatic infections overall (interquartile range 14-50%, prediction interval 2-90%), or in 84 studies based on screening of defined populations (interquartile range 20-65%, prediction interval 4-94%). In 46 studies based on contact or outbreak investigations, the summary proportion asymptomatic was 19% (95% CI 15-25%, prediction interval 2-70%). (2) The secondary attack rate in contacts of people with asymptomatic infection compared with symptomatic infection was 0.32 (95% CI 0.16-0.64, prediction interval 0.11-0-95, 8 studies). (3) In 13 modelling studies fit to data, the proportion of all SARS-CoV-2 transmission from presymptomatic individuals was higher than from asymptomatic individuals. Limitations of the evidence include high heterogeneity and high risks of selection and information bias in studies that were not designed to measure persistently asymptomatic infection, and limited information about variants of concern or in people who have been vaccinated.CONCLUSIONSBased on studies published up to July 2021, most SARS-CoV-2 infections were not persistently asymptomatic and asymptomatic infections were less infectious than symptomatic infections. Summary estimates from meta-analysis may be misleading when variability between studies is extreme and prediction intervals should be presented. Future studies should determine the asymptomatic proportion of SARS-CoV-2 infections caused by variants of concern and in people with immunity following vaccination or previous infection. Without prospective longitudinal studies with methods that minimise selection and measurement biases, further updates with the study types included in this living systematic review are unlikely to be able to provide a reliable summary estimate of the proportion of asymptomatic infections caused by SARS-CoV-2.REVIEW PROTOCOLOpen Science Framework (https://osf.io/9ewys/)AUTHOR SUMMARYWhy was this study done?▪The proportion of people who will remain asymptomatic throughout the course of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (covid-19), is debated.▪Studies that assess people at just one time point overestimate the proportion of true asymptomatic infection because those who go on to develop covid-19 symptoms will be wrongly classified as asymptomatic, but other types of study might underestimate the proportion if, for example, people with symptoms are more likely to be included in a study population.▪The number of published studies about SARS-CoV-2 is increasing continuously, types of studies are changing and, since 2021, vaccines have become available, and variants of concern have emerged.What did the researchers do and find?▪We updated a living systematic review through 6 July 2021, using automated workflows that speed up the review processes, and allow the review to be updated when relevant new evidence becomes available.▪In 130 studies, we found an interquartile range of 14-50% (prediction interval 2-90%) of people with SARS-CoV-2 infection that was persistently asymptomatic; owing to heterogeneity, we did not estimate a summary proportion.▪Contacts of people with asymptomatic SARS-CoV-2 infection are less likely to become infected than contacts of people with symptomatic infection (risk ratio 0.38, 95% CI 0.16-0.64, prediction interval 0.11-0.95, 8 studies).What do these findings mean?▪Up to mid-2021, most people with SARS-CoV-2 were not persistently asymptomatic and asymptomatic infection was less infectious than symptomatic infection.▪In the presence of high between-study variability, summary estimates from meta-analysis may be misleading and prediction intervals should be presented.▪Future studies about asymptomatic SARS-CoV-2 infections caused by variants of concern and in people with immunity following vaccination or previous infection should be specifically designed, using methods to minimise biases in the selection of study participants and in ascertainment, classification and follow-up of symptom status.

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.

Background The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative. Methods To illustrate the development of a meta-learner, we used a dataset of 187,757 people with depression. Using 31 variables, we aimed to predict two outcomes measured 60 days after initiation of antidepressant treatment: severity of depressive symptoms (continuous) and all-cause dropouts (binary). We fitted a ridge regression and a multi-layer perceptron (MLP) deep neural network as two separate prediction models (“base-learners”). We then developed two “meta-learners”, combining predictions from the two base-learners. To compare the performance across the different methods, we calculated mean absolute error (MAE, for continuous outcome) and the area under the receiver operating characteristic curve (AUC, for binary outcome) using bootstrapping. Results Compared to the best performing base-learner (MLP base-learner, MAE at 4.63, AUC at 0.59), the best performing meta-learner showed a 2.49% decrease in MAE at 4.52 for the continuous outcome and a 6.47% increase in AUC at 0.60 for the binary outcome. Conclusions A meta-learner approach may effectively combine multiple prediction models. Choosing between statistical and machine learning models may not be necessary in practice.

Background Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. Objectives Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). Methods We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as “treat none” or “treat all patients with a specific treatment” strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. Results We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the “treat patients according to the prediction model” strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. Conclusions This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. Highlights Decision curve analysis is extended into a (network) meta-analysis framework. Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials. Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined. This extension of decision curve analysis can be applied to (network) meta-analysis–based prediction models to evaluate their use to aid treatment decision making.

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.

We aimed to compare the effects of vitamin C, glucocorticoids, vitamin B1, combinations of these drugs, and placebo or usual care on longer-term mortality in adults with sepsis or septic shock. MEDLINE, Embase, CENTRAL, ClinicalTrials.gov and WHO-ICTRP were searched. The final search was carried out on September 3rd, 2021. Multiple reviewers independently selected randomized controlled trials (RCTs) comparing very-high-dose vitamin C (≥ 12 g/day), high-dose vitamin C (

ObjectiveA network meta-analysis (NMA) usually assesses multiple outcomes across several treatment comparisons. TheVitruvian plotaims to facilitate communication of multiple outcomes from NMAs to patients and clinicians.MethodsWe developed this tool following the recommendations on the communication of benefit–risk information from the available literature. We collected and implemented feedback from researchers, statisticians, methodologists, clinicians and people with lived experience of physical and mental health issues.ResultsWe present theVitruvian plot, which graphically presents absolute estimates and relative performance of competing interventions against a common comparator for several outcomes of interest. We use two alternative colour schemes to highlight either the strength of statistical evidence or the confidence in the evidence. Confidence in the evidence is evaluated across six domains (within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence) using the Confidence in Network Meta-Analysis (CINeMA) system.ConclusionsTheVitruvian plotallows reporting of multiple outcomes from NMAs, with colourings appropriate to inform credibility of the presented evidence.

BACKGROUND Prevalence measures the occurrence of any health condition, exposure or other factors related to health. The experience of COVID-19, a new disease caused by SARS-CoV-2, has highlighted the importance of prevalence studies, for which issues of reporting and methodology have traditionally been neglected. OBJECTIVE This communication highlights key issues about risks of bias in the design and conduct of prevalence studies and in reporting them, using examples about SARS-CoV-2 and COVID-19. SUMMARY The two main domains of bias in prevalence studies are those related to the study population (selection bias) and the condition or risk factor being assessed (information bias). Sources of selection bias should be considered both at the time of the invitation to take part in a study and when assessing who participates and provides valid data (respondents and non-respondents). Information bias appears when there are systematic errors affecting the accuracy and reproducibility of the measurement of the condition or risk factor. Types of information bias include misclassification, observer and recall bias. When reporting prevalence studies, clear descriptions of the target population, study population, study setting and context, and clear definitions of the condition or risk factor and its measurement are essential. Without clear reporting, the risks of bias cannot be assessed properly. Bias in the findings of prevalence studies can, however, impact decision-making and the spread of disease. The concepts discussed here can be applied to the assessment of prevalence for many other conditions. CONCLUSIONS Efforts to strengthen methodological research and improve assessment of the risk of bias and the quality of reporting of studies of prevalence in all fields of research should continue beyond this pandemic.

BackgroundPrognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings.ObjectivesWe aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data.MethodsWe followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement.ResultsWe selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual’s probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the “treat based on the prognostic model” strategy leads to the highest net benefit and hence is considered the most clinically useful strategy.ConclusionsThe prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.

Background Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. Methods We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. Results We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. Conclusions Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01401-y.

IntroductionMatching 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 analysisWe 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.DiscussionThis 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.

IntroductionWe 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 analysisWe 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.DiscussionThis work will provide an in- depth analysis and an insight into the specific adverse events of individual antidepressants.Ethics and disseminationThis review does not require ethical approval.PROSPERO registration numberCRD42019128141.

IntroductionSystematic reviews with network meta-analysis (NMA; ie, multiple treatment comparisons, indirect comparisons) have gained popularity and grown in number due to their ability to provide comparative effectiveness of multiple treatments for the same condition. The methodological review aims to develop a list of items relating to biases in reviews with NMA. Such a list will inform a new tool to assess the risk of bias in NMAs, and potentially other reporting or quality checklists for NMAs which are being updated.Methods and analysisWe will include articles that present items related to bias, reporting or methodological quality, articles assessing the methodological quality of reviews with NMA, or papers presenting methods for NMAs. We will search Ovid MEDLINE, the Cochrane library and difficult to locate/unpublished literature. Once all items have been extracted, we will combine conceptually similar items, classifying them as referring to bias or to other aspects of quality (eg, reporting). When relevant, reporting items will be reworded into items related to bias in NMA review conclusions, and then reworded as signalling questions.Ethics and disseminationNo ethics approval was required. We plan to publish the full study open access in a peer-reviewed journal, and disseminate the findings via social media (Twitter, Facebook and author affiliated websites). Patients, healthcare providers and policy-makers need the highest quality evidence to make decisions about which treatments should be used in healthcare practice. Being able to critically appraise the findings of systematic reviews that include NMA is central to informed decision-making in patient care.

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 reanalyze 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. TH and GS are funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825162. ME was supported by special project funding (grant 17481) from the Swiss National Science Foundation.

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

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.

Importance The doses of antipsychotic drugs needed for relapse prevention in schizophrenia is a debated issue. Objective To examine dose-response findings in a meta-analysis of randomized clinical trials. Data Sources Studies were identified through the Cochrane Schizophrenia Group's Study-Based Register of Trials (March 9, 2020), PubMed (January 1, 2021), and previous reviews. First authors and/or pharmaceutical companies were contacted for additional information. Study Selection Two reviewers independently selected randomized clinical trials that compared fixed doses of a second-generation antipsychotic, haloperidol, or fluphenazine for relapse prevention in patients with stable schizophrenia. Data Extraction and Synthesis Using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline, all parameters in duplicate were extracted and frequentist dose-response random-effects meta-analyses were conducted. Main Outcomes and Measures Study-defined relapse (primary outcome), rehospitalization, Positive and Negative Syndrome Scale or Brief Psychiatric Rating Scale total score reduction from baseline, all-cause discontinuation, and dropouts due to adverse events. Results Evidence from 72 dose arms from 26 studies with 4776 participants was analyzed. The efficacy-related dose-response curves had a hyperbolic shape meaning that the probability to relapse decreased rapidly with doses of up to 5-mg/d risperidone equivalent (relative relapse risk, 0.43; 95% CI, 0.31-0.57; standardized mean difference for Positive and Negative Syndrome Scale total score reduction, -0.55; 95% CI, -0.68 to -0.41), but flattened thereafter. In contrast, dropouts due to adverse events continued to increase beyond this dose (relative risk at 5 mg/d, 1.38; 95% CI, 0.87-2.55; relative risk at 15 mg/d, 2.68; 95% CI, 1.49-4.62). In a subgroup analysis of patients in remission, a plateau was reached earlier, at approximately 2.5-mg/d risperidone equivalent. Conclusions and Relevance The findings of this meta-analysis suggest that doses higher than approximately 5-mg/d risperidone equivalent may provide limited additional benefit for relapse prevention but more adverse events. For patients in remission or who are receiving high-potency first-generation antipsychotics, doses as low as 2.5-mg/d risperidone equivalent may be sufficient. However, caution is needed at this low dose end when further decreases of dose may be accompanied by a disproportionally higher relapse risk. Moreover, the observations are averages, and factors such as slow or rapid metabolism, age, illness stage, comorbidities, and drug-drug interactions suggest that individual patients will often need higher or lower doses.

In a quantitative synthesis of studies via meta-analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta-analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data-generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta-analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency.

Objectives This study compares three major elements of evidence-based medicine (EBM) practices, namely evidence synthesis, clinical practice guidelines (CPGs), and real-world prescriptions in the United States, regarding antidepressant treatments of major depression over the past 3 decades. Study Design and Setting We conducted network meta-analyses (NMAs) of antidepressants every 5 years up to 2016 based on a comprehensive data set of double-blind randomized controlled trials. We identified CPGs and extracted their recommendations. We surveyed the prescriptions in the United States at 5-year intervals up to 2015. Results Most drugs recommended by CPGs presented favorable performance in efficacy and acceptability in NMAs. However, CPG recommendations were often in terms of drug classes rather than individual drugs, whereas NMAs suggested distinctive difference between drugs within the same class. The update intervals of all CPGs were longer than 5 years. All the antidepressants prescribed frequently in the United States were recommended by CPGs. However, changes in prescriptions did not correspond to alterations in CPGs or to apparent changes in the effects indicated by NMAs. Many factors including marketing efforts, regulations, or patient values may have played a role. Conclusion Enhancements including accelerating CPG updates and monitoring the impact of marketing on prescriptions should be considered in future EBM implementation.

Background Antipsychotic drugs and especially the newer compounds are known to cause metabolic side effects. However, a comprehensive comparison of the different substances regarding their propensity to cause metabolic side effects in medium- to long-term treatment of schizophrenia is lacking. Methods We will conduct a systematic review and network meta-analysis (NMA). We will include randomized controlled trials (RCTs) in which participants received either placebo or an antipsychotic (i.e. placebo-controlled trials and head-to-head comparisons of drugs). We will include studies in individuals with schizophrenia or related disorders (such as schizophreniform or schizoaffective disorders) at any stage of the disease (acute episode; maintenance phase). We will include studies with a duration of more than 3 months (medium- to long-term treatment). The primary outcome will be the change in body weight. Secondary outcomes will be the further metabolic parameters: fastening glucose, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides. We will search for eligible studies (independent of the publication status) in Cochrane Schizophrenia Group’s Study-Based Register of Trials, which is compiled by regular searches in trial registries and multiple electronic databases from their inception onwards including MEDLINE, EMBASE and PsycINFO. Additionally, we will search previously published systematic reviews and websites of pharmaceutical companies for eligible studies. At least two reviewers will independently conduct the process of study selection and data extraction. We will use the Cochrane Risk of Bias 2 tool to evaluate the risk of bias in studies. We will conduct random-effects NMA within a Bayesian framework to synthesize all evidence for each outcome. We will conduct sensitivity and subgroup analyses to assess the robustness of the findings and to explore heterogeneity. The confidence in the results will be evaluated using the Confidence in Network Meta-Analysis (CINeMA) framework. Discussion This systematic review and network meta-analysis will provide a synthesis of the existing evidence from RCTs how antipsychotic drugs differ in terms of metabolic side effects during medium- to long-term treatment. The findings have the potential to influence the choice of antipsychotic medication made by individuals with schizophrenia and their physicians. Systematic review registration PROSPERO CRD42020175414 Supplementary Information The online version contains supplementary material available at 10.1186/s13643-021-01760-z.

Summary Background Antibiotic prophylaxis is frequently continued for 1 day or more after surgery to prevent surgical site infection. Continuing antibiotic prophylaxis after an operation might have no advantage compared with its immediate discontinuation, and it unnecessarily exposes patients to risks associated with antibiotic use. In 2016, WHO recommended discontinuation of antibiotic prophylaxis after surgery. We aimed to update the evidence that formed the basis for that recommendation. Methods For this systematic review and meta-analysis, we searched MEDLINE, Embase, CINAHL, CENTRAL, and WHO regional medical databases for randomised controlled trials (RCTs) on postoperative antibiotic prophylaxis that were published from Jan 1, 1990, to July 24, 2018. RCTs comparing the effect of postoperative continuation versus discontinuation of antibiotic prophylaxis on the incidence of surgical site infection in patients undergoing any surgical procedure with an indication for antibiotic prophylaxis were eligible. The primary outcome was the effect of postoperative surgical antibiotic prophylaxis continuation versus its immediate discontinuation on the occurrence of surgical site infection, with a prespecified subgroup analysis for studies that did and did not adhere to current best practice standards for surgical antibiotic prophylaxis. We calculated summary relative risks (RRs) with corresponding 95% CIs using a random effects model (DerSimonian and Laird). We evaluated heterogeneity with the χ2 test, I2, and τ2, and visually assesed publication bias with a contour-enhanced funnel plot. This study is registered with PROSPERO, CRD42017060829. Findings We identified 83 relevant RCTs, of which 52 RCTs with 19 273 participants were included in the primary meta-analysis. The pooled RR of surgical site infection with postoperative continuation of antibiotic prophylaxis versus its immediate discontinuation was 0·89 (95% CI 0·79–1·00), with low heterogeneity in effect size between studies (τ2=0·001, χ2 p=0·46, I2=0·7%). Our prespecified subgroup analysis showed a significant association between the effect estimate and adherence to best practice standards of surgical antibiotic prophylaxis: the RR of surgical site infection was reduced with continued antibiotic prophylaxis after surgery compared with its immediate discontinuation in trials that did not meet best practice standards (0·79 [95% CI 0·67–0·94]) but not in trials that did (1·04 [0·85–1·27]; p=0·048). Whether studies adhered to best practice standards explained all variance in the pooled estimate from the primary meta-analysis. Interpretation Overall, we identified no conclusive evidence for a benefit of postoperative continuation of antibiotic prophylaxis over its discontinuation. When best practice standards were followed, postoperative continuation of antibiotic prophylaxis did not yield any additional benefit in reducing the incidence of surgical site infection. These findings support WHO recommendations against this practice. Funding None.

Network meta-analysis aims to compare multiple interventions by synthesizing studies where each study compares only some of the interventions. This chapter explains how direct and indirect evidence are synthesized to compare interventions linked in a network of trials under the assumptions of consistency and transitivity. It defines the statistical model used for network meta-analysis under consistency, using various perspectives and implementations to shed more light on the model, and describes various ways in which the model may be extended to explore inconsistency. It then shows how network meta-analysis results may be interpreted using tools to rank the interventions. Topics common to all meta-analysis work, such as meta-regression and publication bias, are discussed in the network meta-analysis case, and available software is described.

Background: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements of a hat matrix. It is, however, not fully clear how to generalize this to the percentage contribution of each study to a NMA estimate. Methods: We define the importance of each study for a NMA estimate by the reduction of the estimate's variance when adding the given study to the others. An equivalent interpretation is the relative loss in precision when the study is left out. Importances are values between 0 and 1. An importance of 1 means that the study is an essential link of the pathway in the network connecting one of the treatments with another. Results: Importances can be defined for two-stage and one-stage NMA. These numbers in general do not add to one and thus cannot be interpreted as `percentage contributions'. After briefly discussing other available approaches, we question whether it is possible to obtain unique percentage contributions for NMA. Conclusions: Importances generalize the concept of weights in pairwise meta-analysis in a natural way. Moreover, they are uniquely defined, easily calculated, and have an intuitive interpretation. We give some real examples for illustration.

Background A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). Methods We will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individual-level data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACR-EULAR Boolean or index-based remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a two-stage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a meta-regression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using . Discussion This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients. Systematic review registration PROSPERO registration number pending (ID#157595).

Objectives Symptoms of insomnia are highly prevalent in the elderly. A significant number of pharmacological and non‐pharmacological interventions exist, but, up‐to‐date, their comparative efficacy and safety has not been sufficiently assessed. Methods We integrated the randomized evidence from every available treatment for insomnia in the elderly (>65 years) by performing a network meta‐analysis. Several electronic databases were searched up to May 25, 2019. The two primary outcomes were total sleep time and sleep quality. Data for other 6 efficacy and 8 safety outcomes were also analyzed. Results Fifty‐three RCTs with 6832 participants (75 years old on average) were included, 43 of which examined the efficacy of one or more drugs. Ten RCTs examined the efficacy of non‐pharmacological interventions and were evaluated only with pairwise meta‐analyses because they were disconnected from the network. The overall confidence in the evidence was very low primarily due to the small amount of data per comparison and their sparse connectedness. Several benzodiazepines, antidepressants, and z‐drugs performed better in both primary outcomes, but few comparisons had data from more than one trial. The limited evidence on non‐pharmacological interventions suggested that acupressure, auricular acupuncture, mindfulness‐based stress reduction program, and tart cherry juice were better than their control interventions. Regarding safety, no clear differences were detected among interventions due to large uncertainty. Conclusions Insufficient evidence exists on which intervention is more efficacious for elderly patients with insomnia. More RCTs, with longer duration, making more direct interventions among active treatments and presenting more outcomes are urgently needed.