{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T23:26:52Z","timestamp":1792193212775,"version":"4.4.0"},"reference-count":58,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2002,1,25]],"date-time":"2002-01-25T00:00:00Z","timestamp":1011916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2002,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This tutorial on advanced statistical methods for meta\u2010analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta\u2010analysis 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\u2010analyses as well as meta\u2010regression methods. Several extensions of the models are discussed, like exact likelihood, non\u2010normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta\u2010analysis. All methods are illustrated by a meta\u2010analysis 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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