Teerenstra S, Moerbeek M, Melis R J F, Borm G F
Department of Epidemiology and Biostatistics, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
Stat Med. 2007 Sep 30;26(22):4100-15. doi: 10.1002/sim.2851.
A major methodological reason to use cluster randomization is to avoid the contamination that would arise in an individually randomized design. However, when patient recruitment cannot be completed before randomization of clusters, the non-blindedness of recruiters and patients may cause selection bias, while in the control clusters, it may slow recruitment due to patient or recruiter preferences for the intervention. As a compromise, pseudo cluster randomization has been proposed. Because no insight is available into the relative performance of methods to analyse data obtained from this design, we compared the type I and II error rates of mixed models, generalized estimating equations (GEE) and a paired t-test to those of the estimator originally proposed in this design. The bias in the point estimate and its standard error were also incorporated into this comparison. Furthermore, we evaluated the effect of the weighting scheme and the accuracy of the sample size formula that have been described previously. Power levels of the originally proposed estimator and the unweighted mixed models were in agreement with the sample size formula, but the power of paired t-test fell short. GEE produced too large type I errors, unless the number of clusters was large (>30-40 per arm). The use of the weighting scheme generally enhanced the power, but at the cost of increasing the type I error in mixed models and GEE. We recommend unweighted mixed models as the best compromise between feasibility and power to analyse data from a pseudo cluster randomized trial.
采用整群随机化的一个主要方法学原因是避免在个体随机设计中出现的污染。然而，当在整群随机化之前无法完成患者招募时，招募者和患者的非盲性可能会导致选择偏倚，而在对照整群中，由于患者或招募者对干预措施的偏好，可能会减缓招募进程。作为一种折衷方案，有人提出了伪整群随机化。由于无法了解分析从此设计中获得的数据的方法的相对性能，我们将混合模型、广义估计方程（GEE）和配对t检验的I型和II型错误率与该设计中最初提出的估计器的错误率进行了比较。点估计的偏差及其标准误差也纳入了此比较。此外，我们评估了先前描述的加权方案的效果和样本量公式的准确性。最初提出的估计器和未加权混合模型的检验效能水平与样本量公式一致，但配对t检验的效能不足。GEE产生的I型错误过大，除非整群数量很大（每组>30 - 40个）。加权方案的使用通常会提高检验效能，但代价是增加了混合模型和GEE中的I型错误。我们建议将未加权混合模型作为分析伪整群随机试验数据的可行性和检验效能之间的最佳折衷方案。
