Estimating population-level effects of a vaccine is challenging because there may be interference, that is, the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.

中文翻译：

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适应部分干扰和审查的疫苗效果的逆概率加权估计

估计疫苗对人群的影响具有挑战性，因为可能存在干扰，即一个人的结果可能取决于另一个人的疫苗接种状况。部分干扰发生在个体可以被划分成组时，干扰只发生在组内。在没有干扰的情况下，逆概率加权 (IPW) 估计量通常用于推断暴露或治疗的因果效应。Tchetgen Tchetgen 和 VanderWeele 提出了一种改进的 IPW 估计量，用于在存在部分干扰的情况下计算因果效应。受孟加拉国霍乱疫苗研究的启发，本文考虑将 Tchetgen Tchetgen 和 VanderWeele IPW 估计量扩展到结果受审查权重逆概率 (IPCW) 右审查的设置。使用比例风险脆弱模型估计审查权重。推导了 IPCW 估计器的大样本属性，并提出了模拟研究，证明了估计器在有限样本中的性能。然后使用这些方法分析霍乱疫苗研究的数据。