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Methodological quality of meta-analyses indexed in PsycINFO: leads for enhancements: a meta-epidemiological study.

ABSTRACT: OBJECTIVES:Meta-analyses (MAs) are often used because they are lauded to provide robust evidence that synthesises information from multiple studies. However, the validity of MA conclusions relies on the procedural rigour applied by the authors. Therefore, this meta-research study aims to characterise the methodological quality and meta-analytic practices of MAs indexed in PsycINFO. DESIGN:A meta-epidemiological study. PARTICIPANTS:We evaluated a random sample of 206 MAs indexed in the PsycINFO database in 2016. PRIMARY AND SECONDARY OUTCOMES:Two authors independently extracted the methodological characteristics of all MAs and checked their quality according to the 16 items of the A MeaSurement Tool to Assess systematic Reviews (AMSTAR2) tool for MA critical appraisal. Moreover, we investigated the effect of mentioning Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) on the methodological quality of MAs. RESULTS:According to AMSTAR2 criteria, 95% of the 206 MAs were rated as critically low quality. Statistical methods were appropriate and publication bias was well evaluated in 87% and 70% of the MAs, respectively. However, much improvement is needed in data collection and analysis: only 11% of MAs published a research protocol, 44% had a comprehensive literature search strategy, 37% assessed and 29% interpreted the risk of bias in the individual included studies, and 11% presented a list of excluded studies. Interestingly, the explicit mentioning of PRISMA suggested a positive influence on the methodological quality of MAs. CONCLUSION:The methodological quality of MAs in our sample was critically low according to the AMSTAR2 criteria. Some efforts to tremendously improve the methodological quality of MAs could increase their robustness and reliability.

SUBMITTER: Leclercq V

PROVIDER: S-EPMC7402002 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Methodological quality of meta-analyses indexed in PsycINFO: leads for enhancements: a meta-epidemiological study.

Leclercq Victoria V Beaudart Charlotte C Ajamieh Sara S Tirelli Ezio E Bruyère Olivier O

BMJ open 20200803 8

<h4>Objectives</h4>Meta-analyses (MAs) are often used because they are lauded to provide robust evidence that synthesises information from multiple studies. However, the validity of MA conclusions relies on the procedural rigour applied by the authors. Therefore, this meta-research study aims to characterise the methodological quality and meta-analytic practices of MAs indexed in PsycINFO.<h4>Design</h4>A meta-epidemiological study.<h4>Participants</h4>We evaluated a random sample of 206 MAs ind ...[more]

PMID: 32747348

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Project description:Rationale, aims, and objectivesCOVID-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.MethodsWe 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.ResultsWe 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 I2 statistic was widely used for assessing heterogeneity (92.20%); many meta-analyses had high values of I2 . 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.ConclusionsThe 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.

Project description:BackgroundStudies have shown that industry-sponsored meta-analyses of drugs lack scientific rigour and have biased conclusions. However, these studies have been restricted to certain medical specialities. We compared all industry-supported meta-analyses of drug-drug comparisons with those without industry support.MethodsWe searched PubMed for all meta-analyses that compared different drugs or classes of drugs published in 2004. Two authors assessed the meta-analyses and independently extracted data. We used a validated scale for judging the methodological quality and a binary scale for judging conclusions. We divided the meta-analyses according to the type of support in 3 categories: industry-supported, non-profit support or no support, and undeclared support.ResultsWe included 39 meta-analyses. Ten had industry support, 18 non-profit or no support, and 11 undeclared support. On a 0-7 scale, the median quality score was 6 for meta-analyses with non-profit or no support and 2.5 for the industry-supported meta-analyses (P < 0.01). Compared with industry-supported meta-analyses, more meta-analyses with non-profit or no support avoided bias in the selection of studies (P = 0.01), more often stated the search methods used to find studies (P = 0.02), searched comprehensively (P < 0.01), reported criteria for assessing the validity of the studies (P = 0.02), used appropriate criteria (P = 0.04), described methods of allocation concealment (P = 0.05), described methods of blinding (P = 0.05), and described excluded patients (P = 0.08) and studies (P = 0.15). Forty percent of the industry-supported meta-analyses recommended the experimental drug without reservations, compared with 22% of the meta-analyses with non-profit or no support (P = 0.57).In a sensitivity analysis, we contacted the authors of the meta-analyses with undeclared support. Eight who replied that they had not received industry funding were added to those with non-profit or no support, and 3 who did not reply were added to those with industry support. This analysis did not change the results much.ConclusionTransparency is essential for readers to make their own judgment about medical interventions guided by the results of meta-analyses. We found that industry-supported meta-analyses are less transparent than meta-analyses with non-profit support or no support.

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Methodological quality of meta-analyses indexed in PsycINFO: leads for enhancements: a meta-epidemiological study.

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Methodological quality of meta-analyses indexed in PsycINFO: leads for enhancements: a meta-epidemiological study.

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