title: A refined method for multivariate meta-analysis and meta-regression.
abstract: 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.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Cambridge, U.K.
authors.forename: Daniel
authors.initials: D
authors.lastname: Jackson
authors.forename: Richard D
authors.initials: RD
authors.lastname: Riley
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology; D003198:Computer Simulation; D016001:Confidence Intervals; D004268:DNA-Binding Proteins / Q000097:blood; D018572:Disease-Free Survival; D004720:Endonucleases / Q000097:blood; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015999:Multivariate Analysis; D010510:Periodontal Diseases / Q000628:therapy; D016896:Treatment Outcome
chemical_list: D004268:DNA-Binding Proteins; C071447:ERCC1 protein, human; D004720:Endonucleases
keywords: multivariate meta-analysis; multivariate t distribution; random effects models; small sample inference