Chang Chia-Rui, Wang Rui
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States.
Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA 02215, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae135.
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 weighing 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, for example, all outcomes from a cluster are missing due to drop-out of the cluster, these approaches 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 healthcare 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 of the multiple propensity score models postulated at each clustering level is correctly specified. We evaluate the performance of the proposed method through extensive simulations and illustrate its use with a CRT evaluating a Malaria risk-reduction intervention in rural Madagascar.
整群随机试验（CRT）的分析可能会因结局数据的信息性缺失而变得复杂。诸如逆概率加权广义估计方程等方法已被提出，通过对每个整群中观察到的个体结局数据进行加权来处理信息性缺失问题。这些现有方法主要关注个体层面出现缺失的情况，且每个整群都有部分或全部个体结局被观察到。例如，在存在整群缺失的情况下，由于整群退出导致该整群的所有结局都缺失，这些方法会忽略这种整群层面的缺失，如果整群层面的缺失具有信息性，可能会导致有偏的推断。在具有多层次结构的CRT中，比如研究参与者嵌套在诸如医疗服务提供者等子整群中，而子整群又嵌套在诸如诊所等整群中，也会出现多层次的信息性缺失。在本文中，我们基于加权广义估计方程，提出了新的估计量，用于估计CRT中考虑多层次结局数据缺失时的边际治疗效果。我们表明，只要在每个聚类层面假定的多个倾向得分模型中有一个被正确设定，所提出的多层次多重稳健估计量就是一致的且渐近正态分布。我们通过广泛的模拟评估了所提出方法的性能，并以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为例说明了其应用。
