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*

This expository paper explains the philosophy of the indifference-zone approach and the subset-selection approach to ranking and selection procedures. It includes examples of operating characteristic curves and data applications for selection problems based on the binomial and normal distributions. A variety of different models and goals are provided with a list of references.

An Introduction to Ranking and Selection

SUMMARY Bounds for the tails of Dirichlet integrals are established by showing that each integral as a function of the limits is a Schur function. In particular, it is shown how these bounds apply to the simultaneous analysis of variance test and to the multinomial distribution.

Monotonicity properties of Dirichlet integrals with applications to the multinomial distribution and the analysis of variance

Background and Objectives
Given the increased number of treatment options for stage IA lung cancer patients, there is a growing body of literature that focuses on comparing each option's relative impact on quality of life (QoL) The current study seeks to further understand the differences in these patients’ QoL according to surgical approach

Methods
Screening-diagnosed first primary pathologic stage IA non-small-cell lung cancer surgical patients from the I-ELCAP cohort who answered a baseline and 1-year follow-up QoL questionnaire (SF-12) were included in the analysis Thoracotomy patients (N = 85) were compared with VATS patients (N = 15) using paired t-tests and analysis of variance tests

Results
Multivariate analyses indicated no differences in QoL change between the two groups from pre- to post-surgery Physical and emotional role functioning significantly improved among VATS patients and worsened among thoracotomy patients Among thoracotomy patients, a significant decrease in post-surgical physical QoL was observed only in those who underwent lobectomy (−33; 95% CI: −51,−15), not limited resection

Conclusions
Although the sample size is small, preliminary findings underscore that changes in overall QoL are similar in VATS and thoracotomy stage IA lung cancer patients Extension of the resection may be a more relevant factor on QoL post-surgery J Surg Oncol 2017;115:173–180 © 2016 Wiley Periodicals, Inc

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The impact of resection method and patient factors on quality of life among stage IA non‐small cell lung cancer surgical patients

Recent concern about the effectiveness of alternative treatments in medicine and health, in education, in psychology, and in the social sciences has led to a consideration of how to combine or synthesize the results of independent studies. Historically, integration of independent results focused on how to combine p-values. More recently the emphasis has been on estimating effect sizes, which in turn has motivated a variety of alternative models depending on the experimental conditions. We here review the development of the field with an emphasis of diagnostics and future research.

Meta-analysis: current issues in research synthesis.

(1996). When Does A*A = B*B and Why Does One Want to Know? The American Mathematical Monthly: Vol. 103, No. 6, pp. 470-482.

Ingram Olkin

Papers

An Introduction to Ranking and Selection

Monotonicity properties of Dirichlet integrals with applications to the multinomial distribution and the analysis of variance

The impact of resection method and patient factors on quality of life among stage IA non‐small cell lung cancer surgical patients

Meta-analysis: current issues in research synthesis.

When Does AA = BB and Why Does One Want to Know?