Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N Martin Ave, Tucson, AZ, 85724, USA.
Trials. 2020 Feb 7;21(1):148. doi: 10.1186/s13063-020-4114-9.
Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model's appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random.
We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics.
When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081.
Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally.
ClinicalTrials.gov, ID: NCT02804698.
集群随机试验（CRT）是一种用于测试干预措施的设计方法，其中个体随机化不合适。混合模型重复测量（MMRM）是一种用于具有纵向连续结局的个体随机试验的流行选择。这种模型的吸引力在于避免模型指定不当，并且对于完全随机或随机缺失的数据是无偏的。
我们通过为集群添加随机截距来将 MMRM 扩展到集群随机试验，并进行了一项模拟实验，以研究数据随机缺失时的统计特性。我们模拟了连续结局且在基线和三个干预后时间点进行测量的集群随机试验数据。我们改变了集群数量、集群大小、集群内相关性、缺失和数据生成模型。我们用一个关于糖尿病患者心血管疾病预防的集群随机试验的例子来演示 MMRM-CRT。
当模拟最终时间点的治疗效果时，我们发现当数据完整且数据随机缺失时，估计是无偏的。方差分量也基本无偏。当模拟无效假设时，我们发现尽管对于少数特定情况，I 型错误高达 0.081，但I 型错误在很大程度上是名义上的。
尽管有人断言当受试者有超过两次重复测量时，这种模型不合适，但我们发现了相反的证据。我们得出结论，对于纵向测量的连续结局的集群随机试验，MMRM 是一种很好的分析选择。
ClinicalTrials.gov，ID：NCT02804698。
