David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

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Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews.

Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7

In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1).



Box 1

Conceptual issues in the evolution from QUOROM to PRISMA

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? 

Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis.

Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4

Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted?

A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

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Measuring inconsistency in meta-analyses

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

Meta-Analysis in Clinical Trials*

Peakedness in Multivariate Distributions

This summary corresponds to the translation into Spanish of the Special Communication published in the Journal of the American Medical Association in August 1996, along with the editorial published in the same issue "How to report Randomized Controlled Trials. The Consort Statement". It describes the Consolidated Standars for Preparation of Controlled Clinical Trials, prepared by a work group made up of members of the SORT Group and of the Asilomar Work Group, along with the editor of a magazine and the author of the report on a clinical trial. The work was carried out by means of a Delphy process and the result was a check list and a process diagram. The check list is made up of 21 items that mainly refer to methods, results and discussions on the report of a controlled clinical trial, identifying the necessary information in order to be able to evaluate the internal and external value of the report, judging the improvement to be positive for the patient, the editors and the reviewers of the magazines.

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Mejora de la calidad de los informes de los ensayos clínicos aleatorio controlados. Recomendaciones del grupo de trabajo CONSORT

Multivariate exponential and geometric distributions with limited memory

From its inception in the social science literature, metaanalysis has become a popular methodology. One might even call it a minor industry. Some view it as the wave of the future, others consider it a disaster. But as with most extreme points of view, the truth probably lies somewhere in between. What is somewhat perplexing is why this topic has generated so much controversy. Surely comparable deficiencies exist in many other statistical procedures. Perhaps the name appears to be somewhat pretentious, all-encompassing, guaranteeing new insights. The name macro-analysis (in contrast to micro-analysis) might have been less provocative. One of the most commonly stated criticisms of metaanalysis is that it may assume away differences among studies and be insensitive to the pitfalls of combining studies. It is also noted that one cannot obtain good results from a collection of poor studies. These reasonable caveats might well be applied to all science. In spite of criticisms of particular methods of cumulation, the fact remains that science is cumulative. Even the "hardest" of sciences, such as physics, have produced numerous published determinations of physical constants such as those concerned with gravitational force, dating from experiments by Cavendish in 1798 to present-day research. Synthesis of past results is central to almost any research, and indeed, it is unthinkable to embark on any current research without regard for, and integration of, the past. One might argue that altogether too many papers quote previous work without much verification of the conclusions. As with almost any

Ingram Olkin

Papers

Peakedness in Multivariate Distributions

Mejora de la calidad de los informes de los ensayos clínicos aleatorio controlados. Recomendaciones del grupo de trabajo CONSORT

Multivariate exponential and geometric distributions with limited memory

Book Review: Meta Analysis: A Review and A New View:

Meta-analysis: A review and a new view