Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome
Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome
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DOI：
10.1002/sim.7137
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发表时间：
2017-01-30
影响因子：
2
通讯作者：
Kahan, Brennan C.
中科院分区：
文献类型：
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作者：
Morgan, Katy E.;Forbes, Andrew B.;Kahan, Brennan C.
在集群随机交叉(CRXO)试验中，集群在一段时间内以随机序列接受多种治疗。在这样的试验中，同一组中的患者之间通常存在相关性。此外，在一个集群内，同一时期的患者之间的相似性可能比其他时期的患者更相似。我们证明，有必要在分析中考虑这些相关性，以获得正确的I型错误率。然后，我们使用模拟来比较从两个周期的CRXO设计中分析二元结果的不同方法。我们的模拟表明，对于没有随机影响的簇内周期的分层模型，它没有考虑任何额外的周期内相关性，在许多情况下表现不佳，具有严重夸大的类型I错误。在存在额外周期内相关性的情况下，对簇和簇内周期具有随机影响的分层模型只有在簇数目较多时才具有正确的类型I错误；当簇数目较小时，错误率被夸大。我们还发现，在所考虑的任何情况下，广义估计方程都不能给出正确的错误率。未加权的群集级汇总回归总体表现最好，将所有场景的错误率保持在接近5%的水平，尽管当存在额外的周期内相关性时，尤其是对于少量的群组，它会失去动力。我们的模拟研究结果表明，在CRXO试验中对两个水平的聚集进行建模是重要的，并且任何额外的周期内相关性都应该被考虑在内。版权所有(C)2016 John Wiley&Sons，Ltd.
In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright (C) 2016 John Wiley & Sons, Ltd.