Background: Assessment of heterogeneity is essential in systematic reviews and meta-analyses of clinical trials. The most commonly used heterogeneity measure, I2, provides an estimate of the proportion of variability in a meta-analysis that is explained by differences between the included trials rather than by sampling error. Recent studies have raised concerns about the reliability of I2 estimates, due to their dependence on the precision of included trials and time-dependent biases. Authors have also advocated use of 95 % confidence intervals (CIs) to express the uncertainty associated with I2 estimates. However, no previous studies have explored how many trials and events are required to ensure stable and reliable I2 estimates, or how 95...
This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity...
Abstract Background Confidence intervals for the betw...
BACKGROUND: Recently developed measures such as I2 and H allow the evaluation of the impact of heter...
BACKGROUND: Assessment of heterogeneity is essential in systematic reviews and meta-analyses of clin...
AbstractVariance between studies in a meta-analysis will exist. This heterogeneity may be of clinica...
Background: The heterogeneity statistic I2, interpreted as the percentage of variability due to hete...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
AbstractVariance between studies in a meta-analysis will exist. This heterogeneity may be of clinica...
Contains fulltext : 137008.pdf (Publisher’s version ) (Closed access)Variance betw...
BACKGROUND: The heterogeneity statistic I(2), interpreted as the percentage of variability due to he...
This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity...
Abstract Background The heterogeneity statistic I2, interpreted as the percentage of variability due...
This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity...
Abstract Background Confidence intervals for the betw...
BACKGROUND: Recently developed measures such as I2 and H allow the evaluation of the impact of heter...
BACKGROUND: Assessment of heterogeneity is essential in systematic reviews and meta-analyses of clin...
AbstractVariance between studies in a meta-analysis will exist. This heterogeneity may be of clinica...
Background: The heterogeneity statistic I2, interpreted as the percentage of variability due to hete...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, metho...
AbstractVariance between studies in a meta-analysis will exist. This heterogeneity may be of clinica...
Contains fulltext : 137008.pdf (Publisher’s version ) (Closed access)Variance betw...
BACKGROUND: The heterogeneity statistic I(2), interpreted as the percentage of variability due to he...
This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity...
Abstract Background The heterogeneity statistic I2, interpreted as the percentage of variability due...
This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity...
Abstract Background Confidence intervals for the betw...
BACKGROUND: Recently developed measures such as I2 and H allow the evaluation of the impact of heter...