Randomized control trials (RCTs) often use clustered designs, where intact clusters (such as classroom, schools, or treatment centers) are randomly assigned to treatment and control conditions. Hierarchical linear models (HLMs) are used almost universally to estimate the effects in such experiments. While study designs that utilize intact clusters have many potential advantages, there is little guidance in the literature on how to respond when cluster switching induces noncompliance with the randomization protocol. In the presence of noncompliance the intent-to-treat (ITT) effect becomes the estimand of interest. When fitting the HLM, these individuals who switch clusters can be assigned to either their as-assigned cluster (the cluster they belonged to at the time of randomization) or their as-treated cluster (the cluster they belonged to at the time the outcome was collected). We show analytically and via simulation, that using the as-treated cluster in HLM will bias the estimate of the ITT effect and using the as-assigned cluster will bias the standard error estimates when heterogeneity among clusters is because of heterogeneity in treatment effects. We show that using linear regression with two-way cluster adjusted standard errors can yield unbiased ITT estimates and consistent standard errors regardless of the source of the random effects. We recommend this method replace HLM as the method of choice for testing intervention effects with cluster-randomized trials with noncompliance and cluster switching. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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在集群随机对照试验中切换集群成员资格：对设计和分析的意义。

随机对照试验（RCT）通常使用聚类设计，将完整的聚类（例如教室，学校或治疗中心）随机分配给治疗和控制条件。分层线性模型（HLM）几乎普遍用于估算此类实验的效果。尽管利用完整簇的研究设计具有许多潜在的优势，但是在文献中很少有指导说明当簇切换引起不遵守随机方案时如何应对。在不合规的情况下，意向性治疗（ITT）效果将成为您的关注点。安装HLM时，这些切换集群的个人可以分配给他们的已分配集群（在随机化时它们所属的集群）或他们的已处理集群（收集结果时它们所属的集群）。我们通过分析和仿真显示，当群集之间的异质性是由于处理效果的异质性时，在HLM中使用已处理的群集将对ITT效果的估计产生偏差，而使用已分配的群集将使标准误差估计值产生偏差。我们表明，将线性回归与双向聚类调整后的标准误差一起使用可产生无偏的ITT估计值和一致的标准误差，而与随机效应的来源无关。我们建议使用此方法代替HLM，作为采用不服从和群集切换的群集随机试验来测试干预效果的首选方法。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。