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Cited by 1,580

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

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

van Houwelingen, Hans C., Arends, Lidia R., Stijnen, Theo·2002·Statistics in Medicine
DOI 10.1002/sim.1040
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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. Copyright © 2002 John Wiley & Sons, Ltd.

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Research works printed but not linked: 47

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Metadata

Authors
van Houwelingen, H. C., Arends, L. R., & Stijnen, T.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2002-01-25
Volume
21
Issue
4
Pages
589-624
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.1040
OpenAlex
W2126602143
Cited by
1,580
References
12
FWCI
35.86
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)

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CoChat. (2026). Citation check: Advanced methods in meta‐analysis: multivariate approach and meta‐regression. https://app.cochat.ai/papers/10.1002/sim.1040
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van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta‐analysis: multivariate approach and meta‐regression. Statistics in Medicine. https://doi.org/10.1002/sim.1040

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.