Liu Lan, Hudgens Michael G, Saul Bradley, Clemens John D, Ali Mohammad, Emch Michael E
School of Statistics, University of Minnesota at Twin Cities, Minnsota, U.S.A.
Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.
Stat (Int Stat Inst). 2019;8(1). doi: 10.1002/sta4.214. Epub 2019 Jan 10.
Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings it may be reasonable to assume individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, i.e., there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been proposed of different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modeled. Alternatively, one can estimate the treatment effect using an outcome regression model. In this paper, we propose doubly robust (DR) estimators which utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified. Empirical results are presented to demonstrate the DR property of the proposed estimators, as well as the efficiency gain of DR over IPW estimators when both models are correctly specified. The different estimators are illustrated using data from a study examining the effects of cholera vaccination in Bangladesh.
当对一个个体的治疗（或暴露）影响其他个体的结果时，就会发生干扰。在某些情况下，合理的假设是个体可以被划分为不同的群组，使得不同群组中的个体之间不存在干扰，即存在部分干扰。在存在部分干扰的观察性研究中，已经提出了不同可能治疗效果的逆概率加权（IPW）估计量。然而，IPW估计量的有效性取决于倾向得分是否已知或被正确建模。另外，也可以使用结果回归模型来估计治疗效果。在本文中，我们提出了双稳健（DR）估计量，它同时利用了这两种模型，并且如果其中任何一个模型（不一定是两个模型）被正确设定，那么该估计量是一致的且渐近正态的。我们给出了实证结果，以证明所提出估计量的双稳健特性，以及当两个模型都被正确设定时，双稳健估计量相对于IPW估计量在效率上的提升。我们使用来自一项关于孟加拉国霍乱疫苗接种效果研究的数据来说明不同的估计量。
