Dataset Information

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

ABSTRACT:

Background

Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines.

Results

To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines.

Conclusion

BUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.

SUBMITTER: Beliveau A

PROVIDER: S-EPMC6805536 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

Béliveau Audrey A Boyne Devon J DJ Slater Justin J Brenner Darren D Arora Paul P

BMC medical research methodology 20191022 1

<h4>Background</h4>Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines.<h4>Results</h4>To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysi ...[more]

PMID: 31640567

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Project description:BackgroundEvidence has shown that private industry-sponsored randomized controlled trials (RCTs) and meta-analyses are more likely to report intervention-favourable results compared with other sources of funding. However, this has not been assessed in network meta-analyses (NMAs).ObjectivesTo (a) explore the recommendation rate of industry-sponsored NMAs on their company's intervention, and (b) assess reporting in NMAs of pharmacologic interventions according to their funding type.MethodsDesign: Scoping review of published NMAs with RCTs.Information sourcesWe used a pre-existing NMA database including 1,144 articles from MEDLINE, EMBASE and Cochrane Database of Systematic Reviews, published between January 2013 and July 2018.Study selectionNMAs with transparent funding information and comparing pharmacologic interventions with/without placebo.SynthesisWe captured whether NMAs recommended their own or another company's intervention, classified NMAs according to their primary outcome findings (i.e., statistical significance and direction of effect), and according to the overall reported conclusion. We assessed reporting using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension to NMA (PRISMA-NMA) 32-item checklist. We matched and compared industry with non-industry NMAs having the same research question, disease, primary outcome, and pharmacologic intervention against placebo/control.ResultsWe retrieved 658 NMAs, which reported a median of 23 items in the PRISMA-NMA checklist (interquartile range [IQR]: 21-26). NMAs were categorized as 314 publicly-sponsored (PRISMA-NMA median 24.5, IQR 22-27), 208 non-sponsored (PRISMA-NMA median 23, IQR 20-25), and 136 industry/mixed-sponsored NMAs (PRISMA-NMA median 21, IQR 19-24). Most industry-sponsored NMAs recommended their own manufactured drug (92%), suggested a statistically significant positive treatment-effect for their drug (82%), and reported an overall positive conclusion (92%). Our matched NMAs (25 industry vs 25 non-industry) indicated that industry-sponsored NMAs had favourable conclusions more often (100% vs 80%) and were associated with larger (but not statistically significantly different) efficacy effect sizes (in 61% of NMAs) compared with non-industry-sponsored NMAs.ConclusionsDifferences in completeness of reporting and author characteristics were apparent among NMAs with different types of funding. Publicly-sponsored NMAs had the best reporting and published their findings in higher impact-factor journals. Knowledge users should be mindful of this potential funding bias in NMAs.

Project description:ObjectiveOur objective was to evaluate quality of conduct and reporting of published systematic reviews and meta-analyses in paediatric surgery. We also aimed to identify characteristics predictive of review quality.BackgroundSystematic reviews summarise evidence by combining sources, but are potentially prone to bias. To counter this, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was published to aid in reporting. Similarly, the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) measurement tool was designed to appraise methodology. The paediatric surgical literature has seen an increasing number of reviews over the past decade, but quality has not been evaluated.MethodsAdhering to PRISMA guidelines, we performed a systematic review with a priori design to identify systematic reviews and meta-analyses of interventions in paediatric surgery. From 01/2010 to 06/2016, we searched: MEDLINE, EMBASE, Cochrane, Centre for Reviews and Dissemination, Web of Science, Google Scholar, reference lists and journals. Two reviewers independently selected studies and extracted data. We assessed conduct and reporting using AMSTAR and PRISMA. Scores were calculated as the sum of reported items. We also extracted author, journal and article characteristics, and used them in exploratory analysis to determine which variables predict quality.Results112 articles fulfilled eligibility criteria (53 systematic reviews; 59 meta-analyses). Overall, 68% AMSTAR and 56.8% PRISMA items were reported adequately. Poorest scores were identified with regards a priori design, inclusion of structured summaries, including the grey literature, citing excluded articles and evaluating bias. 13 reviews were pre-registered and 6 in PRISMA-endorsing journals. The following predicted quality in univariate analysis:, word count, Cochrane review, journal h-index, impact factor, journal endorses PRISMA, PRISMA adherence suggested in author guidance, article mentions PRISMA, review includes comparison of interventions and review registration. The latter three variables were significant in multivariate regression.ConclusionsThere are gaps in the conduct and reporting of systematic reviews in paediatric surgery. More endorsement by journals of the PRISMA guideline may improve review quality, and the dissemination of reliable evidence to paediatric clinicians.

Project description:OBJECTIVES:Systematic reviews and meta-analyses are labor-intensive and time-consuming. Automated extraction of quantitative data from primary studies can accelerate this process. ClinicalTrials.gov, launched in 2000, is the world's largest trial repository of results data from clinical trials; it has been used as a source instead of journal articles. We have developed a Web application called EXACT (EXtracting Accurate efficacy and safety information from ClinicalTrials.gov) that allows users without advanced programming skills to automatically extract data from ClinicalTrials.gov in analysis-ready format. We have also used the automatically extracted data to examine the reproducibility of meta-analyses in three published systematic reviews. STUDY DESIGN AND SETTING:We developed a Python-based software application (EXACT) that automatically extracts data required for meta-analysis from the ClinicalTrials.gov database in a spreadsheet format. We confirmed the accuracy of the extracted data and then used those data to repeat meta-analyses in three published systematic reviews. To ensure that we used the same statistical methods and outcomes as the published systematic reviews, we repeated the meta-analyses using data manually extracted from the relevant journal articles. For the outcomes whose results we were able to reproduce using those journal article data, we examined the usability of ClinicalTrials.gov data. RESULTS:EXACT extracted data at ClincalTrials.gov with 100% accuracy, and it required 60% less time than the usual practice of manually extracting data from journal articles. We found that 87% of the data elements extracted using EXACT matched those extracted manually from the journal articles. We were able to reproduce 24 of 28 outcomes using the journal article data. Of these 24 outcomes, we were able to reproduce 83.3% of the published estimates using data at ClinicalTrials.gov. CONCLUSION:EXACT (http://bio-nlp.org/EXACT) automatically and accurately extracted data elements from ClinicalTrials.gov and thus reduced time in data extraction. The ClinicalTrials.gov data reproduced most meta-analysis results in our study, but this conclusion needs further validation.

Dataset Information

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

Background

Results

Conclusion

Publications

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

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