Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighting the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes. In the presence of missing clusters, e.g., all outcomes from a cluster are missing due to drop-out of the cluster, these approaches effectively ignore this cluster-level missingness and can lead to biased inference if the cluster-level missingness is informative. Informative missingness at multiple levels can also occur in CRTs with a multi-level structure where study participants are nested in subclusters such as health care providers, and the subclusters are nested in clusters such as clinics. In this paper, we propose new estimators for estimating the marginal treatment effect in CRTs accounting for missing outcome data at multiple levels based on weighted generalized estimating equations. We show that the proposed multi-level multiply robust estimator is consistent and asymptotically normally distributed provided that one set of the propensity score models is correctly specified. We evaluate the performance of the proposed method through extensive simulation and illustrate its use with a CRT evaluating a Malaria risk-reduction intervention in rural Madagascar.

翻译：集群随机试验（CRTs）的分析可能因信息性缺失结局数据而变得复杂。已有方法如逆概率加权广义估计方程，通过加权每个集群中观察到的个体结局数据来应对信息性缺失。这些现有方法主要关注缺失发生在个体层面的情况，且每个集群的部分或全部个体结局数据可被观测。当存在缺失集群（例如，因集群退出导致其所有结局数据缺失）时，这些方法实际上忽略了集群层面的缺失，若集群层面缺失具有信息性，则可能导致有偏推断。在多水平结构的CRTs中（如研究参与者嵌套于子集群如医疗服务提供者，子集群嵌套于集群如诊所），多水平信息性缺失也可能发生。本文提出基于加权广义估计方程的新估计量，用于估计CRTs中考虑多水平缺失结局数据下的边际处理效应。我们证明，若至少一组倾向得分模型设定正确，所提出的多水平多重稳健估计量具有一致性和渐近正态性。通过大量模拟评估该方法性能，并将其应用于一项评估马达加斯加农村地区疟疾风险降低干预措施的CRT中。