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Open AccessJournal ArticleDOI

Measuring inconsistency in meta-analyses

TLDR
A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract
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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Citations
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Journal ArticleDOI

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Eleftheria Zeggini, +110 more
- 30 Mar 2008 - 
TL;DR: The results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D, and detect at least six previously unknown loci with robust evidence for association.
Journal ArticleDOI

Interpretation of random effects meta-analyses.

TL;DR: Summary estimates of treatment effect from random effects meta-analysis give only the average effect across all studies, but inclusion of prediction intervals, which estimate the likely effect in an individual setting, could make it easier to apply the results to clinical practice.
Journal ArticleDOI

A systematic review of mental disorder, suicide, and deliberate self harm in lesbian, gay and bisexual people

TL;DR: A systematic review and meta-analysis of the prevalence of mental disorder, substance misuse, suicide, suicidal ideation and deliberate self harm in LGB people revealed that lesbian and bisexual women were particularly at risk of substance dependence.
Journal ArticleDOI

SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials.

TL;DR: SGLT2i have moderate benefits on atherosclerotic major adverse cardiovascular events that seem confined to patients with established atheroscerotic cardiovascular disease, however, they have robust benefits on reducing hospitalisation for heart failure and progression of renal disease regardless of existing atherosclerosis or a history of heart failure.
References
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Journal ArticleDOI

Quantifying heterogeneity in a meta‐analysis

TL;DR: It is concluded that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity, and one or both should be presented in publishedMeta-an analyses in preference to the test for heterogeneity.
Journal ArticleDOI

The combination of estimates from different experiments.

TL;DR: The problem of making a combined estimate has been discussed previously by Cochran and Yates and Cochran (1937) for agricultural experiments, and by Bliss (1952) for bioassays in different laboratories as discussed by the authors.
Journal ArticleDOI

Tamoxifen for early breast cancer: An overview of the randomised trials

TL;DR: The absolute improvement in recurrence was greater during the first 5 years, whereas the improvement in survival grew steadily larger throughout the first 10 years, and these benefits appeared to be largely irrespective of age, menopausal status, daily tamoxifen dose, and of whether chemotherapy had been given to both groups.
Journal Article

Tamoxifen for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group

Anthony Howell
- 16 May 1998 - 
TL;DR: There have been many randomised trials of adjuvant tamoxifen among women with early breast cancer, and an updated overview of their results is presented in this paper, which approximately doubles the amount of evidence from trials of about 5 years of tamoxifier and, taking all trials together, on events occurring more than 5 years after randomisation.
Journal ArticleDOI

Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis.

TL;DR: In this paper, the authors evaluated standard error, precision (inverse of standard error), variance, inverse of variance, sample size and log sample size (vertical axis) and log odds ratio, log risk ratio and risk difference (horizontal axis).
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