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

BMC Medical Research Methodology

Brennan C Kahan¹ 

[1] Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK

关键词: Mantel-Haenszel;    Generalised estimating equations;    Random effects;    Fixed-effects;    Multicentre trials;    Randomised controlled trial;    Binary outcomes;   

Others  :  866463
DOI  :  10.1186/1471-2288-14-20

 received in 2013-07-05, accepted in 2014-02-03,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Kahan; licensee BioMed Central Ltd.

【 图 表 】

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