Network Dependence Can Lead to Spurious Associations and Invalid Inference

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in <i>spurious associations due to network dependence</i>. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation.

健康科学与社会科学领域的研究者通常默认观测值相互独立，即便他们所采用的便利样本（convenience sample）仅从单个或少量社区、学校、医院等群体中招募受试者。此类研究的典型范例便是弗雷明汉心脏研究（Framingham Heart Study, FHS）。这类样本的诸多局限已为学界熟知，但由社会网络联结引发的统计相依性问题，此前尚未得到足够重视与解决。
我们的研究表明，除了保守性不足的方差估计（anticonservative variance estimation）之外，这类问题还可能引发**因网络依赖性导致的虚假关联（spurious associations due to network dependence）**。我们改编了一项原本用于空间自相关（spatial autocorrelation）的统计检验方法，对数千篇依托弗雷明汉心脏研究数据发表的高影响力论文中的部分研究开展了网络依赖性检验。
结果显示，数十年来依托弗雷明汉心脏研究数据开展的冠心病、其他健康结局以及同伴影响等相关研究中，部分研究可能因未被意识到的网络依赖性问题，出现虚假关联、点估计误差偏大以及保守性不足的统计推断等问题。此类问题并非弗雷明汉心脏研究所独有；随着心理学、医学等领域的研究者不断应对可重复性危机，这一未被认知的无效统计推断来源，理应成为学界讨论的重要议题之一。