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

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Statistics in medicine 2002-02, Vol.21 (4), p.589-624
Hauptverfasser: van Houwelingen, Hans C., Arends, Lidia R., Stijnen, Theo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1040