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Outputs from living evidence syntheses projects have been used widely during the pandemic by guideline developers to form evidence-based recommendations. However, the needs of different stakeholders cannot be accommodated by solely providing pre-defined non amendable numerical summaries. Stakeholders also need to understand the data and perform their own exploratory analyses. This requires resources, time, statistical expertise, software knowledge as well as relevant clinical expertise to avoid spurious conclusions. To assist them, we created the "metaCOVID" application which, based on automation processes, facilitates the fast exploration of the data and the conduct of sub-analyses tailored to end-users needs. "metaCOVID" has been created in R and is freely available as an R-Shiny application. Based on the COVID-NMA platform (https://covid-nma.com/) the application conducts living meta-analyses of randomized controlled trials related to COVID-19 treatments and vaccines for several outcomes. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. We illustrate "metaCOVID" through three examples involving well-known treatments and vaccines for COVID-19. The application is freely available from https://covid-nma.com/metacovid/.

Abstract Background One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach. Methods We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( $$N = 40\,237$$ N = 40 237 , 836 events). Results Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances. Conclusions For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Abstract Background This study examined the synthesis methods used in meta-analyses pooling data from observational studies (OSs) and randomised controlled trials (RCTs) from various medical disciplines. Methods We searched Medline via PubMed to identify reports of systematic reviews of interventions, including and pooling data from RCTs and OSs published in 110 high-impact factor general and specialised journals between 2015 and 2019. Screening and data extraction were performed in duplicate. To describe the synthesis methods used in the meta-analyses, we considered the first meta-analysis presented in each article. Results Overall, 132 reports were identified with a median number of included studies of 14 [9–26]. The median number of OSs was 6.5 [3–12] and that of RCTs was 3 [1–6]. The effect estimates recorded from OSs (i.e., adjusted or unadjusted) were not specified in 82% (n = 108) of the meta-analyses. An inverse-variance common-effect model was used in 2% (n = 3) of the meta-analyses, a random-effects model was used in 55% (n = 73), and both models were used in 40% (n = 53). A Poisson regression model was used in 1 meta-analysis, and 2 meta-analyses did not report the model they used. The mean total weight of OSs in the studied meta-analyses was 57.3% (standard deviation, ± 30.3%). Only 44 (33%) meta-analyses reported results stratified by study design. Of them, the results between OSs and RCTs had a consistent direction of effect in 70% (n = 31). Study design was explored as a potential source of heterogeneity in 79% of the meta-analyses, and confounding factors were investigated in only 10% (n = 13). Publication bias was assessed in 70% (n = 92) of the meta-analyses. Tau-square was reported in 32 meta-analyses with a median of 0.07 [0–0.30]. Conclusion The inclusion of OSs in a meta-analysis on interventions could provide useful information. However, considerations of several methodological and conceptual aspects of OSs, that are required to avoid misleading findings, were often absent or insufficiently reported in our sample.

Abstract Background Preprints are increasingly used to disseminate research results, providing multiple sources of information for the same study. We assessed the consistency in effect estimates between preprint and subsequent journal article of COVID-19 randomized controlled trials. Methods The study utilized data from the COVID-NMA living systematic review of pharmacological treatments for COVID-19 (covid-nma.com) up to July 20, 2022. We identified randomized controlled trials (RCTs) evaluating pharmacological treatments vs. standard of care/placebo for patients with COVID-19 that were originally posted as preprints and subsequently published as journal articles. Trials that did not report the same analysis in both documents were excluded. Data were extracted independently by pairs of researchers with consensus to resolve disagreements. Effect estimates extracted from the first preprint were compared to effect estimates from the journal article. Results The search identified 135 RCTs originally posted as a preprint and subsequently published as a journal article. We excluded 26 RCTs that did not meet the eligibility criteria, of which 13 RCTs reported an interim analysis in the preprint and a final analysis in the journal article. Overall, 109 preprint–article RCTs were included in the analysis. The median (interquartile range) delay between preprint and journal article was 121 (73–187) days, the median sample size was 150 (71–464) participants, 76% of RCTs had been prospectively registered, 60% received industry or mixed funding, 72% were multicentric trials. The overall risk of bias was rated as ‘some concern’ for 80% of RCTs. We found that 81 preprint–article pairs of RCTs were consistent for all outcomes reported. There were nine RCTs with at least one outcome with a discrepancy in the number of participants with outcome events or the number of participants analyzed, which yielded a minor change in the estimate of the effect. Furthermore, six RCTs had at least one outcome missing in the journal article and 14 RCTs had at least one outcome added in the journal article compared to the preprint. There was a change in the direction of effect in one RCT. No changes in statistical significance or conclusions were found. Conclusions Effect estimates were generally consistent between COVID-19 preprints and subsequent journal articles. The main results and interpretation did not change in any trial. Nevertheless, some outcomes were added and deleted in some journal articles.

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.

Abstract Background There is no consensus on the most efficient catheter ablation (CA) strategy for patients with atrial fibrillation (AF). The objective of this study was to compare the efficacy and safety of different CA strategies for AF ablation through network meta-analysis (NMA). Methods A systematic search of PubMed, Web of Science, and CENTRAL was performed up to October 5th, 2020. Randomized controlled trials (RCT) comparing different CA approaches were included. Efficacy was defined as arrhythmia recurrence after CA and safety as any reported complication related to the procedure during a minimum follow-up time of 6 months. Results In total, 67 RCTs (n = 9871) comparing 19 different CA strategies were included. The risk of recurrence was significantly decreased compared to pulmonary vein isolation (PVI) alone for PVI with renal denervation (RR: 0.60, CI: 0.38–0.94), PVI with ganglia-plexi ablation (RR: 0.62, CI: 0.41–0.94), PVI with additional ablation lines (RR: 0.8, CI: 0.68–0.95) and PVI in combination with bi-atrial modification (RR: 0.32, CI: 0.11–0.88). Strategies including PVI appeared superior to non-PVI strategies such as electrogram-based approaches. No significant differences in safety were observed. Conclusions This NMA showed that PVI in combination with additional CA strategies, such as autonomic modulation and additional lines, seem to increase the efficacy of PVI alone. These strategies can be considered in treating patients with AF, since, additionally, no differences in safety were observed. This study provides decision-makers with comprehensive and comparative evidence about the efficacy and safety of different CA strategies. Systematic review registration PROSPERO registry number: CRD42020169494 .

Introduction There have been important advances in the use of electroconvulsive therapy (ECT) to treat major depressive episodes. These include variations to the type of stimulus the brain regions stimulated, and the stimulus parameters (eg, stimulus duration/pulse width). Our aim is to investigate ECT types using a network meta-analysis (NMA) approach and report on comparative treatment efficacy, cognitive side effects and acceptability.Method We will conduct a systematic review to identify randomised controlled trials that compared two or more ECT protocols to treat depression. This will be done using the following databases: Embase, MEDLINE PubMed, Web of Science, Scopus, PsycINFO, Cochrane CENTRAL and will be supplemented by personal contacts with researchers in the field. All authors will be contacted to provide missing information. Primary outcomes will be symptom severity on a validated continuous clinician-rated scale of depression, cognitive functioning measured using anterograde verbal recall, and acceptability calculated using all-cause drop-outs. Secondary outcomes will include response and remission rates, autobiographical memory following a course of ECT, and anterograde visuospatial recall.Bayesian random effects hierarchical models will compare ECT types. Additional meta-regressions may be conducted to determine the impact of effect modifiers and patient-specific prognostic factors if sufficient data are available.Discussion This NMA will facilitate clinician decision making and allow more sophisticated selection of ECT type according to the balance of efficacy, cognitive side effects and acceptability.Ethics This systematic review and NMA does not require research ethics approval as it will use published aggregate data and will not collect nor disclose individually identifiable participant data.PROSPERO registration number CRD42022357098.

Abstract 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.

Network meta‐analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large‐sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two‐stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks. [ABSTRACT FROM AUTHOR]

Introduction Work absenteeism due to chronic non-malignant pain (CNMP) is a major societal and individual cause of concern that requires effective treatments.Objective We present a protocol for a systematic review and network meta-analysis (NMA) aiming to compare available interventions for return to work (RTW) in adults with CNMP.Methods and analysis PubMed, Embase, PsycINFO, Web of Knowledge and Cochrane Central Register of Controlled Trials databases will be searched till 31 August 2020 for randomised controlled trials (RCTs) examining interventions for RTW outcomes among patients with CNMP. Two independent investigators will search the databases, perform data extraction and assess the methodological quality of the selected RCTs. The primary outcome will be RTW, if possible, full-time or part-time after work absence due to chronic pain from baseline to the last available follow-up. Secondary outcomes will include self-reported workability or work capacity, or self-reported physical functioning and quality of life as measured by any validated scale. Pairwise meta-analysis and NMA will be conducted for each outcome using a random-effects model. For the primary outcomes, we will also obtain the ranking of all competing interventions within each NMA using surface under the cumulative ranking curve. The assumption of coherence (ie, that direct and indirect evidence are in statistical agreement) will be examined using both a local and a global approach. We will also conduct subgroup and meta-regression analyses, whenever feasible, to investigate the unexplained variation in effect size. The comparison-adjusted funnel plot will be used to evaluate small-study effects. The overall quality of evidence will be rated with the Confidence in Network Meta-Analysis tool. Data analysis will be conducted using Stata V.16.0.Ethics and dissemination This systematic review does not require ethical approval since it will not disseminate any private patient data. The results of this study will be disseminated through peer-reviewed publication.PROSPERO registration number CRD42020171429.

BACKGROUND:The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. METHODOLOGY:CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. CONCLUSIONS:Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.

Although catheter ablation (CA) is an established treatment for paroxysmal atrial fibrillation (PAF), there is no consensus regarding the most efficient CA strategy. The objective of this network meta-analysis (NMA) was to compare the efficacy and safety of different CA strategies for PAF. A systematic search was performed in PubMed, Web of Science, and CENTRAL until the final search date, 5 October 2020. Randomised controlled trials (RCT) comparing different CA strategies and methods for pulmonary vein isolation (PVI) were included. Efficacy was defined as lack of arrhythmia recurrence after CA and safety as any reported complication related to the procedure during a minimum follow-up time of six months. In total, 43 RCTs comparing 11 different CA strategies involving 6701 patients were included. The risk of recurrence was significantly decreased in comparison with PVI with radiofrequency only for the following treatments: PVI with adjuvant ablation (RR: 0.79, CI: 0.65–0.97) and PVI with sympathetic modulation (RR: 0.64, CI: 0.46–0.88). However, PVI with radiofrequency was superior to non-PVI strategies (RR: 1.65, CI: 1.2–2.26). No statistically significant difference was found in safety between different CA strategies. Concerning different PVI strategies, no difference was observed either in efficacy or in safety between tested strategies. This NMA suggests that different PVI strategies are generally similar in terms of efficacy, while PVI with additional ablation or sympathetic modulation may be more effective than PVI alone. This study provides decision-makers with insights into the efficacy and safety of different CA strategies.