Ukoumunne Obioha C, Carlin John B, Gulliford Martin C
Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute and Department of Paediatrics, University of Melbourne, Australia.
Stat Med. 2007 Aug 15;26(18):3415-28. doi: 10.1002/sim.2769.
We used simulation to compare accuracy of estimation and confidence interval coverage of several methods for analysing binary outcomes from cluster randomized trials. The following methods were used to estimate the population-averaged intervention effect on the log-odds scale: marginal logistic regression models using generalized estimating equations with information sandwich estimates of standard error (GEE); unweighted cluster-level mean difference (CL/U); weighted cluster-level mean difference (CL/W) and cluster-level random effects linear regression (CL/RE). Methods were compared across trials simulated with different numbers of clusters per trial arm, numbers of subjects per cluster, intraclass correlation coefficients (rho), and intervention versus control arm proportions. Two thousand data sets were generated for each combination of design parameter values. The results showed that the GEE method has generally acceptable properties, including close to nominal levels of confidence interval coverage, when a simple adjustment is made for data with relatively few clusters. CL/U and CL/W have good properties for trials where the number of subjects per cluster is sufficiently large and rho is sufficiently small. CL/RE also has good properties in this situation provided a t-distribution multiplier is used for confidence interval calculation in studies with small numbers of clusters. For studies where the number of subjects per cluster is small and rho is large all cluster-level methods may perform poorly for studies with between 10 and 50 clusters per trial arm.
我们采用模拟方法比较了几种分析整群随机试验二元结局的方法在估计准确性和置信区间覆盖方面的表现。以下方法用于估计对数优势比尺度上的总体平均干预效应：使用广义估计方程并采用信息三明治标准误估计的边际逻辑回归模型（GEE）；未加权的整群水平平均差（CL/U）；加权的整群水平平均差（CL/W）以及整群水平随机效应线性回归（CL/RE）。在每个试验组具有不同整群数量、每个整群的受试者数量、组内相关系数（rho）以及干预组与对照组比例的模拟试验中对这些方法进行了比较。针对设计参数值的每种组合生成了2000个数据集。结果表明，当对整群数量相对较少的数据进行简单调整时，GEE方法通常具有可接受的性质，包括接近名义水平的置信区间覆盖。对于每个整群的受试者数量足够大且rho足够小的试验，CL/U和CL/W具有良好的性质。在这种情况下，如果在整群数量较少的研究中使用t分布乘数进行置信区间计算，CL/RE也具有良好的性质。对于每个整群的受试者数量少且rho大的研究，对于每个试验组有10至50个整群的研究，所有整群水平方法可能表现不佳。
