How do you adjust heterogeneity?
9.5. 3 Strategies for addressing heterogeneity
- Check again that the data are correct. Severe heterogeneity can indicate that data have been incorrectly extracted or entered into RevMan.
- Do not do a meta-analysis.
- Explore heterogeneity.
- Ignore heterogeneity.
- Perform a random-effects meta-analysis.
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.
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.
Which is the best measure of heterogeneity in statistics?
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)…
Can a null hypothesis be examined by heterogeneity?
It is arguably not possible to examine the null hypothesis that all studies are evaluating the same effect, by considering the only the summary data from the studies: The heterogeneity test results should be considered alongside a qualitative assessment of the combinability of studies in a systematic review.