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January 25, 2002Statistics in Medicine1,595 citations

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

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HHHans C. van HouwelingenLALidia R. ArendsTSTheo Stijnen

Key Points

  • The tutorial aims to present advanced statistical techniques for analyzing both univariate and bivariate treatment effects in meta-analyses.
  • Utilizes general linear mixed models with approximate likelihood for analysis.
  • Discusses extensions such as exact likelihood and non-normal mixtures for multiple endpoints.
  • Illustrates methods through a meta-analysis of BCG vaccine efficacy against tuberculosis.
  • Demonstrates that standard software can fit all approximate likelihood models.
  • Illustrates advanced methods' applicability with real data on BCG vaccine against tuberculosis.
  • Highlights the advantages of Bayesian methods in enhancing meta-analysis.

Abstract

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.

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Cite This Study

Houwelingen et al. (2002) studied this question.

synapsesocial.com/papers/69d8ef5b183921ebcaae405chttps://doi.org/10.1002/sim.1040
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