A multilevel model framework for meta-analysis of clinical trials with binary outcomes

Authors
Turner, R. M., Omar, R. Z., Yang, M., Goldstein, H., Thompson, S. G.
Year
2000
Journal
Statistics in Medicine, 26:3-4, 353-363
DOI
10.1002/1097-0258(20001230)19:24<3417::AID-SIM614>3.0.CO;2-L
Abstract

In this paper we explore the potential of multilevel models for meta-analysis of trials with binary outcomes for both summary data, such as log-odds ratios, and individual patient data. Conventional fixed effect and random effects models are put into a multilevel model framework, which provides maximum likelihood or restricted maximum likelihood estimation. To exemplify the methods, we use the results from 22 trials to prevent respiratory tract infections; we also make comparisons with a second example data set comprising fewer trials. Within summary data methods, confidence intervals for the overall treatment effect and for the between-trial variance may be derived from likelihood based methods or a parametric bootstrap as well as from Wald methods; the bootstrap intervals are preferred because they relax the assumptions required by the other two methods. When modelling individual patient data, a bias corrected bootstrap may be used to provide unbiased estimation and correctly located confidence intervals; this method is particularly valuable for the between-trial variance. The trial effects may be modelled as either fixed or random within individual data models, and we discuss the corresponding assumptions and implications. If random trial effects are used, the covariance between these and the random treatment effects should be included; the resulting model is equivalent to a bivariate approach to meta-analysis. Having implemented these techniques, the flexibility of multilevel modelling may be exploited in facilitating extensions to standard meta-analysis methods.

Number of levels
2
Model data structure
Response types
Multivariate response model?
Yes
Longitudinal data?
No
Substantive discipline
Impact

Key paper on meta analysis showing how multilevel models with discrete responses can be used.

Paper submitted by
Harvey Goldstein, Graduate School of Education, University of Bristol, h.goldstein@bristol.ac.uk
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