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Advanced methods inmeta‐analysis:multivariate approachand meta‐regression Hans C. van HouwelingenLidia R. ArendsTheo Stijnen2002 год

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

Страница публикацииПубликация в OpenAlex
Аннотация: 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.
Год издания: 2002
Авторы: Hans C. van Houwelingen, Lidia R. Arends, Theo Stijnen
Издательство: Wiley
Источник: Statistics in Medicine
Ключевые слова: Meta-analysis and systematic reviews, Statistical Methods and Bayesian Inference, Statistical Methods in Clinical Trials
Другие ссылки: Statistics in Medicine (HTML)
PubMed (HTML)