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

BMC Med Res Methodol. 2014 Feb 10;14:20. doi: 10.1186/1471-2288-14-20.

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.

MeSH terms

  • Bacterial Infections / drug therapy*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Deoxyribonucleases / therapeutic use*
  • Humans
  • Models, Statistical
  • Multicenter Studies as Topic / methods*
  • Pleura / microbiology
  • Randomized Controlled Trials as Topic / methods*
  • Research Design
  • Tissue Plasminogen Activator / therapeutic use*

Substances

  • Deoxyribonucleases
  • Tissue Plasminogen Activator