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.
|Title:||Meta-analysis of continuous outcome data from individual patients.|
|Keywords:||Alzheimer Disease, Bayes Theorem, Cholinesterase Inhibitors, Cognition, Humans, Meta-Analysis as Topic, Models, Biological, Models, Statistical, Randomized Controlled Trials as Topic, Regression Analysis, Tacrine, Treatment Outcome|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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