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Meta‐analysis of continuous outcome data from individual patients
Author(s) -
Higgins Julian P. T.,
Whitehead Anne,
Turner Rebecca M.,
Omar Rumana Z.,
Thompson Simon G.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.918
Subject(s) - covariate , random effects model , meta analysis , outcome (game theory) , computer science , bayesian probability , multilevel model , statistics , meta regression , mixed model , econometrics , medicine , machine learning , artificial intelligence , mathematics , mathematical economics
Meta‐analyses using individual patient data are becoming increasingly common and have several advantages over meta‐analyses of summary statistics. We explore the use of multilevel or hierarchical models for the meta‐analysis of continuous individual patient outcome data from clinical trials. A general framework is developed which encompasses traditional meta‐analysis, as well as meta‐regression and the inclusion of patient‐level covariates for investigation of heterogeneity. Unexplained variation in treatment differences between trials is considered as random. We focus on models with fixed trial effects, although an extension to a random effect for trial is described. The methods are illustrated on an example in Alzheimer's disease in a classical framework using SAS PROC MIXED and MLwiN, and in a Bayesian framework using BUGS. Relative merits of the three software packages for such meta‐analyses are discussed, as are the assessment of model assumptions and extensions to incorporate more than two treatments. Copyright © 2001 John Wiley & Sons, Ltd.