title: Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?
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.
authors: Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK. b.kahan@qmul.ac.uk.
authors: Brennan C
authors: BC
authors: Kahan
mesh_terms: D001424:Bacterial Infections / Q000188:drug therapy*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003851:Deoxyribonucleases / Q000627:therapeutic use*; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods*; D010994:Pleura / Q000382:microbiology; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D010959:Tissue Plasminogen Activator / Q000627:therapeutic use*
chemical_list: D003851:Deoxyribonucleases; D010959:Tissue Plasminogen Activator