1{2 "key": "vanhouwelingen2002advanced",3 "retrieved": "2026-10-20",4 "via": "doi",5 "message": {6 "indexed": {7 "date-parts": [8 [9 2026,10 10,11 1612 ]13 ],14 "date-time": "2026-10-16T23:26:52Z",15 "timestamp": 1792193212775,16 "version": "4.4.0"17 },18 "reference-count": 58,19 "publisher": "Wiley",20 "issue": "4",21 "license": [22 {23 "start": {24 "date-parts": [25 [26 2002,27 1,28 2529 ]30 ],31 "date-time": "2002-01-25T00:00:00Z",32 "timestamp": 101191680000033 },34 "content-version": "vor",35 "delay-in-days": 0,36 "URL": "http://onlinelibrary.wiley.com/termsAndConditions#vor"37 }38 ],39 "content-domain": {40 "domain": [],41 "crossmark-restriction": false42 },43 "short-container-title": [44 "Statistics in Medicine"45 ],46 "published-print": {47 "date-parts": [48 [49 2002,50 2,51 2852 ]53 ]54 },55 "abstract": "<jats:title>Abstract</jats:title>\n <jats:p>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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