Contents
Why is heterogeneity bad in meta-analysis?
The presence of substantial heterogeneity in a meta-analysis is always of interest. On the one hand, it may indicate that there is excessive clinical diversity in the studies included, and that it is inappropriate to derive an estimate of overall effect from that particular set of studies.
Why is it important to test statistical heterogeneity in a meta-analysis?
Assessment of the consistency of effects across studies is an essential part of meta-analysis. Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.
How is heterogeneity calculated in a meta-analysis?
The classical measure of heterogeneity is Cochran’s Q, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method. Q is distributed as a chi-square statistic with k (numer of studies) minus 1 degrees of freedom.
What is the χ 2 test of heterogeneity?
In short, Cochran’s Q Statistic (χ 2 test of heterogeneity, df = k-1) is a weighted sum of the squared deviations (Borenstein et al. 2007). It has poor power to detect heterogeneity among a small number of studies. The Inconsistency Index (I 2) is the percentage of total variation across studies that is due to heterogeneity (Higgins et al., 2003).
Can a pooled effect be interpreted in a heterogeneous study?
This allows to calculate the pooled effect, defined as the mean of the true effect size distribution. The random-effects model always allows us to calculate a pooled effect size, even if the studies are very heterogeneous. Yet, it does not tell us if this pooled effect can be interpreted in a meaningful way.
How are between study heterogeneity used in random effects?
The random-effects model assumes that between-study heterogeneity causes the true effect sizes of studies to differ. It therefore includes an estimate of τ 2 τ 2, which quantifies this variance in true effects. This allows to calculate the pooled effect, defined as the mean of the true effect size distribution.