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 …

/pdf/measuring-inconsistency-in-meta-analyses-4iysmttp08.pdf

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

/pdf/bias-in-meta-analysis-detected-by-a-simple-graphical-test-5erh428s5c.pdf

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.

/pdf/the-prisma-statement-for-reporting-systematic-reviews-and-3j53e5oe33.pdf

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

The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.

/pdf/cochrane-handbook-for-systematic-reviews-of-interventions-1xbdy6uvru.pdf

Cochrane Handbook for Systematic Reviews of Interventions

Summary. Procedures for testing the equality of two dependent intraclass correlation co-efficients computed from the same sample of subjects are developed and evaluated. It is demonstrated that an extended test procedure based on a normalizing and variance stabilizing transformation of the intraclass correlation coefficient developed by Konishi has consistently greater power than a procedure based on Fisher's classical Z-transformation and also has power which is comparable with that of the likelihood ratio test. Two examples are provided.

Testing the equality of dependent intraclass correlation coefficients

Background
Tacrolimus (formerly FK 506) was first used clinically in 1989 to successfully replace cyclosporine in hepatic transplant recipients who were experiencing intractable rejection or as the baseline drug from the time of operation. After extensive pilot experience, an institutional review board-mandated clinical trial comparing cyclosporine with tacrolimus was performed.

http://d-scholarship.pitt.edu/5199/1/31735062126614.pdf

The Pittsburgh randomized trial of tacrolimus compared to cyclosporine for hepatic transplantation.

Sample size requirements are provided for designs of studies in which clusters are randomized within each of several strata, where cluster size itself may be a stratifying factor. The approach generalizes a formula derived by Woolson et al., which provides sample size requirements for the Cochran-Mantel-Haenszel statistic. Issues of data analysis are also discussed.

Sample size requirements for stratified cluster randomization designs

Cluster randomization trials

The Ontario Exercise-Heart Collaborative Study was a multicenter randomized clinical trial of high Intensity exercise for the prevention of recurrent myocardial infarction in men. The 4-year recurrence rate among 379 patients on a program of high intensity exercise did not differ significantly from that among 354 control patients on a program of light exercise, despite the greater reduction in heart rate in the former group. The relative odds of recurrence in the high intensity group were 1.09, with 95% confidence limits of 0.61 and 1.96.

Allan Donner

Papers

Testing the equality of dependent intraclass correlation coefficients

The Pittsburgh randomized trial of tacrolimus compared to cyclosporine for hepatic transplantation.

Sample size requirements for stratified cluster randomization designs

Cluster randomization trials

Relation of exercise to the recurrence rate of myocardial infarction in men: Ontario exercise-heart collaborative study