Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how?

Open Access Research article Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how? Brennan C Kahan Correspondence: Brennan C Kahan b.kahan@qmul.ac.uk Author Affiliations Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK MRC Clinical Trials Unit at UCL, 125 Kingsway, London WC2B 6NH, UK BMC Medical Research Methodology 2014, 14:20 doi:10.1186/1471-2288-14-20 The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2288/14/20 Received: 5 July 2013 Accepted: 3 February 2014 Published: 10 February 2014 © 2014 Kahan; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Formula display: Abstract Background It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. Methods We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study. Results The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios. Conclusions With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres