Meta-analysis of continuous outcome data from individual patients.
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.
Higgins
J P
JP
MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 2SR, U.K. julian.higgins@mrc-bsu.cam.ac.uk
Whitehead
A
A
Turner
R M
RM
Omar
R Z
RZ
Thompson
S G
SG
D000544:Alzheimer Disease / Q000188:drug therapy; D001499:Bayes Theorem; D002800:Cholinesterase Inhibitors / Q000494:pharmacology / Q000627:therapeutic use; D003071:Cognition / Q000187:drug effects; D006801:Humans; D015201:Meta-Analysis as Topic; D008954:Models, Biological; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D013619:Tacrine / Q000494:pharmacology / Q000627:therapeutic use; D016896:Treatment Outcome
D002800:Cholinesterase Inhibitors; D013619:Tacrine