Journal
STATISTICS IN MEDICINE
Volume 33, Issue 4, Pages 541-554Publisher
WILEY
DOI: 10.1002/sim.5957
Keywords
multivariate meta-analysis; multivariate t distribution; random effects models; small sample inference
Categories
Funding
- UK Medical Research Council [U105260558]
- MRC Methodology Research Grant in Multivariate Meta-analysis [MR/J013595/1]
- MRC [MR/J013595/1, G0800808, MC_U105260558] Funding Source: UKRI
- Medical Research Council [MR/J013595/1, MC_U105260558, G0800808] Funding Source: researchfish
Ask authors/readers for more resources
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright (c) 2013 John Wiley & Sons, Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available