Format

Send to

Choose Destination
Stat Med. 2001 Aug 15;20(15):2219-41.

Meta-analysis of continuous outcome data from individual patients.

Author information

1
MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 2SR, U.K. julian.higgins@mrc-bsu.cam.ac.uk

Abstract

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.

PMID:
11468761
DOI:
10.1002/sim.918
[Indexed for MEDLINE]

Supplemental Content

Loading ...
Support Center