Open synthesis and the coronavirus pandemic in 2020

Haddaway, Neal R.; Akl, Elie A.; Page, Matthew J.; Welch, Vivian; Keenan, Ciara

doi:10.1016/j.jclinepi.2020.06.032

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…The evidence synthesis is achieved via meta‐analyses, which play a critical role in developing new research by determining whether the proposed study is necessary and helping design the study 12,13 . Systematic reviews and meta‐analyses are considered of the highest quality and can be viewed as a lens through which evidence is evaluated 14‐17 …”

Section: Introductionmentioning

confidence: 99%

“…Even if only randomized controlled trials (RCTs) are synthesized, systematic reviews and meta‐analyses themselves are essentially observational studies, which are subject to reporting bias 21,22 . It is critical to properly perform and adequately report systematic reviews and meta‐analyses using rigorous methods, 23 particularly during the fast‐evolving pandemic 6,16,24‐28 . To standardize and improve the quality of reporting, the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) statement was introduced in 2009 29 .…”

Section: Introductionmentioning

confidence: 99%

“…12,13 Systematic reviews and meta-analyses are considered of the highest quality and can be viewed as a lens through which evidence is evaluated. [14][15][16][17] However, systematic reviews and meta-analyses do not always provide a rationale for their methodology; if not used properly, they could produce ambiguous results and exacerbate research errors. [18][19][20] Even if only randomized controlled trials (RCTs) are synthesized, systematic reviews and meta-analyses themselves are essentially observational studies, which are subject to reporting bias.…”

Section: Introductionmentioning

confidence: 99%

“…21,22 It is critical to properly perform and adequately report systematic reviews and meta-analyses using rigorous methods, 23 particularly during the fast-evolving pandemic. 6,16,[24][25][26][27][28] To standardize and improve the quality of reporting, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was introduced in 2009. 29 Similar guidelines, such as meta-analysis of observational studies in epidemiology (MOOSE), can be used for other specific types of research.…”

Section: Introductionmentioning

confidence: 99%

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Methodological assessment of systematic reviews and meta‐analyses on COVID‐19: A meta‐epidemiological study

Rosenberger

Lin

2021

Evaluation Clinical Practice

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Rationale, aims, and objectives COVID‐19 has caused an ongoing public health crisis. Many systematic reviews and meta‐analyses have been performed to synthesize evidence for better understanding this new disease. However, some concerns have been raised about rapid COVID‐19 research. This meta‐epidemiological study aims to methodologically assess the current systematic reviews and meta‐analyses on COVID‐19. Methods We searched in various databases for systematic reviews with meta‐analyses published between 1 January 2020 and 31 October 2020. We extracted their basic characteristics, data analyses, evidence appraisal, and assessment of publication bias and heterogeneity. Results We identified 295 systematic reviews on COVID‐19. The median time from submission to acceptance was 33 days. Among these systematic reviews, 73.9% evaluated clinical manifestations or comorbidities of COVID‐19. Stata was the most used software programme (43.39%). The odds ratio was the most used effect measure (34.24%). Moreover, 28.14% of the systematic reviews did not present evidence appraisal. Among those reporting the risk of bias results, 14.64% of studies had a high risk of bias. Egger's test was the most used method for assessing publication bias (38.31%), while 38.66% of the systematic reviews did not assess publication bias. The I 2 statistic was widely used for assessing heterogeneity (92.20%); many meta‐analyses had high values of I 2 . Among the meta‐analyses using the random‐effects model, 75.82% did not report the methods for model implementation; among those meta‐analyses reporting implementation methods, the DerSimonian‐Laird method was the most used one. Conclusions The current systematic reviews and meta‐analyses on COVID‐19 might suffer from low transparency, high heterogeneity, and suboptimal statistical methods. It is recommended that future systematic reviews on COVID‐19 strictly follow well‐developed guidelines. Sensitivity analyses may be performed to examine how the synthesized evidence might depend on different methods for appraising evidence, assessing publication bias, and implementing meta‐analysis models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methodological assessment of systematic reviews and meta‐analyses on COVID‐19: A meta‐epidemiological study

Rosenberger

Lin

2021

Evaluation Clinical Practice

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“…However, despite the mass production of meta-analyses, it has been found that many meta-analyses need improvements in their methodological quality [5][6][7][8][9][10]. This is a particularly crucial issue in the COVID-19 pandemic because of the concerns about the expedited peer-review process [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy

Zhao

Lin

2021

BioMedInformatics

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Systematic reviews and meta-analyses have been increasingly used to pool research findings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statistical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from statistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appropriate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting prediction intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage methods. We use worked examples to illustrate these good practices. Relevant statistical code is also provided. The conventional and alternative methods could produce noticeably different point and interval estimates in some meta-analyses and thus affect their conclusions. In such cases, researchers should interpret the results from conventional methods with great caution and consider using alternative methods.

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Methods for conducting a living evidence profile on mpox: An evidence map of the literature

Pussegoda,

Corrin,

Baumeister

et al. 2024

Cochrane Evidence Synthesis and Methods

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BackgroundIn May of 2022, several cases of mpox were identified in multiple nonendemic countries and on July 23, 2022 the World Health Organization declared mpox a Public Health Emergency of International Concern. During the first six months of the outbreak there was an urgent need to have up‐to‐date synthesized evidence on mpox to inform public health decision‐making. At this point, evidence is changing too quickly for traditional evidence synthesis methods, as systematic reviews were out‐of‐date before publication. This paper describes the framework developed to manage and maintain a living evidence profile (LEP) to systematically identify, classify and synthesize evidence on a broad range of mpox topics at a rapid pace as the outbreak unfolded.MethodsThe LEP framework was based on principles of evidence synthesis, risk assessment, priority epidemiological parameters for infectious disease modeling and consultation with experts. The framework consisted of a systematic search conducted twice weekly; study selection; categorization into pre‐determined foci and data extraction; integration and synthesis of evidence; internal peer‐review and dissemination to stakeholders.ResultsBetween April 14 and December 15, 2022, 2287 citations were identified, 687 were primary research studies or surveillance reports on the 2022 mpox outbreak and 496 were included in the final LEP. Each study was mapped to one of 32 foci and evidence was narratively synthesized. From June to December 2022, 23 LEPs were produced (approximately weekly) along with a searchable database of extracted data of the mpox literature. They were disseminated globally to public health researchers and decision‐makers to inform public health response efforts.ConclusionsThe LEP framework is applicable to other public health emergencies when a rapid synthesis cycle is required because the evidence is evolving quickly. This efficient methodology for creating up‐to‐date summaries of the current evidence during the first few months of an outbreak or emergency supports public health decision‐making and response activities.

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Open synthesis and the coronavirus pandemic in 2020

Cited by 12 publications

References 34 publications

Methodological assessment of systematic reviews and meta‐analyses on COVID‐19: A meta‐epidemiological study

Methodological assessment of systematic reviews and meta‐analyses on COVID‐19: A meta‐epidemiological study

Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy

Methods for conducting a living evidence profile on mpox: An evidence map of the literature

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