Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.
Department of Biostatistics, University of Washington, Seattle, Washington.
Biometrics. 2022 Jun;78(2):777-788. doi: 10.1111/biom.13459. Epub 2021 Apr 14.
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.
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态。当个体可以被分为群组时，就会发生部分干扰，并且只有在群组内才会发生干扰。在不存在干扰的情况下，常用逆概率加权（Inverse Probability Weighting，简称 IPW）估计量来推断暴露或治疗的因果效应。Tchetgen Tchetgen 和 VanderWeele 提出了一种在存在部分干扰的情况下用于因果效应的修正 IPW 估计量。受孟加拉国霍乱疫苗研究的启发，本文考虑将 Tchetgen Tchetgen 和 VanderWeele 的 IPW 估计量扩展到使用逆概率删失权重（Inverse Probability of Censoring Weights，简称 IPCW）的情况下，该情况下结局受到右删失的影响。删失权重使用比例风险脆弱性模型进行估计。推导了 IPCW 估计量的大样本性质，并进行了模拟研究，以展示在有限样本中估计量的性能。然后，这些方法被用于分析来自霍乱疫苗研究的数据。
