Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

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Bias in meta-analysis detected by a simple, graphical test

Systematic review and meta-analysis

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.

Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.

The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

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The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

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The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

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The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

In previous articles we have focused on the potentials, principles, and pitfalls of meta-analysis of randomised controlled trials.1 2 3 4 5 Meta-analysis of observational data is, however, also becoming common. In a Medline search we identified 566 articles (excluding those published as letters) published in 1995 and indexed with the medical subject heading (MeSH) term “meta-analysis.” We randomly selected 100 of these articles and examined them further. Sixty articles reported on actual meta-analyses, and 40 were methodological papers, editorials, and traditional reviews (1). Among the meta-analyses, about half were based on observational studies, mainly cohort and case-control studies of medical interventions or aetiological associations.

View this table:

Characteristics of 100 articles randomly selected from articles published in 1995 and indexed in Medline with keyword “meta-analysis”



The randomised controlled trial is the principal research design in the evaluation of medical interventions. However, aetiological hypotheses—for example, those relating common exposures to the occurrence of disease—cannot generally be tested in randomised experiments. Does breathing other people's tobacco smoke cause lung cancer, drinking coffee cause coronary heart disease, and eating a diet rich in saturated fat cause breast cancer? Studies of such “menaces of daily life”6 use observational designs or examine the presumed biological mechanisms in the laboratory. In these situations the risks involved are generally small, but once a large proportion of the population is exposed, the potential public health implications of these associations—if they are causal—can be striking.

Analyses of observational data also have a role in medical effectiveness research.7 The evidence available from clinical trials will rarely answer all the important questions. Most trials are conducted to establish efficacy and safety of a single agent in a specific clinical situation. Owing to the limited size of such trials, less common adverse effects of drugs may only be detected in case-control …

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Spurious precision? Meta-analysis of observational studies.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(97)02419-7.pdf

Language Bias in Randomised Controlled Trials Published in English and German

Systematic Reviews of Observational Studies

Highly active antiretroviral therapy (HAART), a combination of at least three drugs, has substantially improved the prognosis of HIV-infected patients in industrialized countries. 1–3 In resourcepoor settings in Africa, Asia, and South America, where 90% of people with HIV/AIDS live, access to HAART continues to be limited. It is estimated that one million HIV-1 infected individuals presently receive HAART in low-income and middle-income countries, which represents only 15% of the 6.5 million people urgently in need of such treatment in these settings. 4 International and bilateral initiatives, including WHO’s ‘3 by 5’ target (3 million patients treated by 2005), the Global Fund to fight AIDS, Tuberculosis and Malaria, and the United States President’s Emergency Plan for AIDS Relief (PEPFAR), aim to substantially increase access to antiretroviral treatment in these settings during the next few months. Clinical and epidemiological research has been conducted at the level of selected centres regarding treatment response, 5–8

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Martin Schneider

Papers

Bias in meta-analysis detected by a simple, graphical test

Spurious precision? Meta-analysis of observational studies.

Language Bias in Randomised Controlled Trials Published in English and German

Systematic Reviews of Observational Studies

Cohort Profile: Antiretroviral Therapy in Lower Income Countries (ART-LINC): international collaboration of treatment cohorts.