VACCINES, CONTAGION, AND SOCIAL NETWORKS
VACCINES, CONTAGION, AND SOCIAL NETWORKS
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DOI：
10.1214/17-aoas1023
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发表时间：
2017-06-01
影响因子：
1.8
通讯作者：
VanderWeele, Tyler J.
中科院分区：
文献类型：
--
作者：
Ogburn, Elizabeth L.;VanderWeele, Tyler J.
考虑当结果具有传染性时，一个人的治疗可能对另一个人的结果产生的因果效应，并具体应用于接种疫苗对传染病结果的影响。一个人的疫苗接种对另一个人的结果的影响可以分解为两种不同的因果效应，称为“传染性”和“传染”效应。我们提出了两种不同环境下传染性和传染效应的识别假设和估计或测试程序：(1)使用从独立观察组中取样的数据，(2)使用从单个相互依赖的社会网络中收集的数据。我们提出的社交网络数据的方法需要拟合广义线性模型（GLMs）。glm和其他需要跨主体独立性的统计模型已被广泛用于估计社会网络数据中的因果关系，但由于网络中的主体可能并不独立，因此此类模型的使用通常是无效的，导致推断预计是反保守的。我们描述了一种子抽样方案，该方案确保GLM误差在受试者之间不相关，尽管结果是非独立的。这同时证明了在网络数据中使用glm和相关统计模型的可能性，并突出了它们的局限性。
Consider the causal effect that one individual's treatment may have on another individual's outcome when the outcome is contagious, with specific application to the effect of vaccination on an infectious disease outcome. The effect of one individual's vaccination on another's outcome can be decomposed into two different causal effects, called the "infectiousness" and "contagion" effects. We present identifying assumptions and estimation or testing procedures for infectiousness and contagion effects in two different settings: (1) using data sampled from independent groups of observations, and (2) using data collected from a single interdependent social network. The methods that we propose for social network data require fitting generalized linear models (GLMs). GLMs and other statistical models that require independence across subjects have been used widely to estimate causal effects in social network data, but because the subjects in networks are presumably not independent, the use of such models is generally invalid, resulting in inference that is expected to be anticonservative. We describe a subsampling scheme that ensures that GLM errors are uncorrelated across subjects despite the fact that outcomes are nonindependent. This simultaneously demonstrates the possibility of using GLMs and related statistical models for network data and highlights their limitations.