Supporting information may be found in the online version of this article.
Analysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?†
Article first published online: 30 OCT 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 32, Issue 7, pages 1136–1149, 30 March 2013
How to Cite
Kahan, B. C. and Morris, T. P. (2013), Analysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?. Statist. Med., 32: 1136–1149. doi: 10.1002/sim.5667
- Issue published online: 11 MAR 2013
- Article first published online: 30 OCT 2012
- Manuscript Accepted: 3 OCT 2012
- Manuscript Received: 13 APR 2012
- multicentre trials;
- continuous outcomes;
- fixed effects;
- random effects;
- randomised controlled trials
In multicentre trials, randomisation is often carried out using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis to obtain correct inference. For continuous outcomes, the two primary methods of accounting for centres are fixed-effects and random-effects models. We discuss the differences in interpretation between these two models and the implications that each pose for analysis. We then perform a large simulation study comparing the performance of these analysis methods in a variety of situations. In total, we assessed 378 scenarios. We found that random centre effects performed as well or better than fixed-effects models in all scenarios. Random centre effects models led to increases in power and precision when the number of patients per centre was small (e.g. 10 patients or less) and, in some scenarios, when there was an imbalance between treatments within centres, either due to the randomisation method or to the distribution of patients across centres. With small samples sizes, random-effects models maintained nominal coverage rates when a degree-of-freedom (DF) correction was used. We assessed the robustness of random-effects models when assumptions regarding the distribution of the centre effects were incorrect and found this had no impact on results. We conclude that random-effects models offer many advantages over fixed-effects models in certain situations and should be used more often in practice. Copyright © 2012 John Wiley & Sons, Ltd.