Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids parametric assumptions and permits inference about counterfactual scenarios corresponding to any stochastic policy which modifies the propensity score distribution, and thus may have application across diverse settings. The proposed nonparametric sample splitting estimators allow for flexible data-adaptive estimation of nuisance functions and are consistent and asymptotically normal with parametric convergence rates. Simulation studies demonstrate the finite sample performance of the proposed estimators, and the methods are applied to a cholera vaccine study in Bangladesh.

翻译：基于观测数据推断治疗对生存时间结果的影响具有挑战性，主要源于删失现象和潜在混杂因素的存在。当个体的治疗会影响其他个体的结果时（即存在干扰），挑战进一步加剧。在某些场景中，个体可能被分组为聚类，使得仅假设干扰在聚类内部发生是合理的，即存在聚类干扰。本文提出了能够同时处理混杂因素、删失结果和聚类干扰的方法。该方法避免了参数化假设，允许对任何修改倾向得分分布的随机策略所对应的反事实场景进行推断，因此可适用于多样化的应用场景。所提出的非参数样本分割估计器允许对干扰函数进行灵活的数据自适应估计，具有参数级收敛速度的一致性和渐近正态性。模拟研究验证了所提估计器在有限样本下的性能，并将该方法应用于孟加拉国的一项霍乱疫苗研究。