Multivariate meta-analysis for non-linear and other multi-parameter associations
Article first published online: 16 JUL 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 29, pages 3821–3839, 20 December 2012
How to Cite
Gasparrini, A., Armstrong, B. and Kenward, M. G. (2012), Multivariate meta-analysis for non-linear and other multi-parameter associations. Statist. Med., 31: 3821–3839. doi: 10.1002/sim.5471
- Issue published online: 23 NOV 2012
- Article first published online: 16 JUL 2012
- Manuscript Accepted: 11 MAY 2012
- Manuscript Received: 9 AUG 2011
- Medical Research Council (UK). Grant Numbers: G1002296, G0701030
- multivariate analysis;
- multivariate meta-analysis;
In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd.