Advanced methods in meta-analysis: multivariate approach and meta-regression

• Published in: Statistics in medicine Vol. 21; no. 4; pp. 589 - 624
• Main Authors: van Houwelingen, Hans C., Arends, Lidia R., Stijnen, Theo
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 28.02.2002; Wiley
• Subjects: Bayes Theorem; BCG Vaccine - immunology; BCG Vaccine - standards; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Humans; Medical sciences; Medical statistics; meta-analysis; Meta-Analysis as Topic; meta-regression; Multivariate Analysis; multivariate random effects models; Statistics as Topic - methods; Tuberculosis - prevention & control; Human; Statistical analysis; Treatment efficiency; Vaccination; BCG; Regression analysis; Statistics; Metaanalysis; Statistical method; Random effect; Bayes test; Medical application; Multivariable system
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.1040

This tutorial on advanced statistical methods for meta‐analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta‐analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood, we discuss methods to analyse univariate as well as bivariate treatment effects in meta‐analyses as well as meta‐regression methods. Several extensions of the models are discussed, like exact likelihood, non‐normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta‐analysis. All methods are illustrated by a meta‐analysis concerning the efficacy of BCG vaccine against tuberculosis. All analyses that use approximate likelihood can be carried out by standard software. We demonstrate how the models can be fitted using SAS Proc Mixed. Copyright © 2002 John Wiley & Sons, Ltd.