Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?
Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?
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DOI：
10.1186/1471-2288-14-20
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发表时间：
2014-02-10
影响因子：
4
通讯作者：
Kahan BC
中科院分区：
文献类型：
--
作者：
Kahan BC
在多中心随机试验的分析中，通常需要考虑中心效应，但目前尚不清楚在二元结局的试验中，哪种分析方法最好。我们比较了四种分析方法（固定效应模型，随机效应模型，广义估计方程（GEE）和Mantel-Haenszel）的性能，使用重新分析先前报告的随机试验（MIST 2）和大型模拟研究。MIST 2的重新分析发现，固定效应和Mantel-Haenszel导致许多患者因过度分层而从分析中脱落（Mantel-Haenszel的脱落率高达69%，固定效应的脱落率高达33%）。相反，随机效应和GEE将所有患者纳入分析，但GEE未达到收敛。不同分析方法之间的估计治疗效应和p值差异很大。模拟研究发现，大多数分析方法在少数中心都能很好地发挥作用。在大量中心的情况下，固定效应在许多情况下导致有偏估计和I类错误率膨胀，在某些情况下，与其他分析方法相比，Mantel-Haenszel失去了功效。相反，随机效应和GEE在所有情景下都给出了标称I型错误率和良好的功效，并且通常与固定效应或Mantel-Haenszel一样好或更好。然而，这仅适用于具有非稳健标准误差（SE）的GEE;使用稳健的“三明治”估计量会导致大多数情况下I型错误率膨胀。对于少数中心，我们建议使用固定效应、随机效应或具有非稳健SE的GEE。随机效应和具有非稳健SE的GEE应在中等或大量中心使用。
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. 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. 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. 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.
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影响因子：
7.2
作者：
Adams, G;Gulliford, MC;Campbell, MJ
通讯作者：
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作者：
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