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*

SYNOPTIC ABSTRACTOrdinary ridge regression is considered as an alternative to least squares regression for the analysis of multicollinear data. When the ridge factor is estimated from the data, we obtain the asymptotic distribution of several ridge factors, and propose a new ridge factor based on asymptotic considerations. The mean squared errors of operational ordinary ridge regression estimators are evaluated asymptotically, and we propose several statistics useful for the selection of ridge factors.

Asymptotic aspects of ordinary ridge regression

: Let x(1) < or = ... x(n) and y(1) < or = ...y(m) be samples of order statistics from two populations F1 and F2 respectively. When the scores are pooled and ordered, we address the question 'what is the probability that the r largest x order statistics appear on a shortlist of k individuals?' This question underlies the selection of students, or of candidates for a job, and has implications in the selection process. This model is studied for general F1 and F2. Tables are given to calculate probabilities when F1 and F2 are normal distributions that differ only by location, and also when F1 and F2 are normal distributions with different means and variances, but there is only one x-value. (CP)

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On Making the Shortlist for the Selection of Candidates

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A Conversation with Margaret Martin

This chapter discusses multivariate models for effect sizes. A key feature of multivariate procedures is that they deal with all the correlated effect sizes simultaneously. A disadvantage of these multivariate techniques is that they usually require knowledge of the correlations between variables—information that is not always available. In a few instances, such correlations may actually be available. For example, test-norming studies may provide very good estimates of correlations between subscales of psychological tests. Such estimates can be treated as known values to provide the correlations necessary to use the methods given in this chapter. The chapter presents the multivariate distribution of a vector of effect sizes derived from correlated observations. It also discusses the estimation of a common effect size from a vector of correlated estimates. The chapter explores methods for estimating effect sizes from a series of studies in which a few studies provide several correlated estimates and other studies provide a single estimate.

Ingram Olkin

Papers

Asymptotic aspects of ordinary ridge regression

On Making the Shortlist for the Selection of Candidates

A Conversation with Margaret Martin

Multivariate Models for Effect Sizes

A Probabilistic Proof of a Theorem of Schur