Abstract

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 spurious associations due to network dependence. 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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

摘要

健康和社会科学领域的研究人员通常假设观察是独立的，即使依赖于从一个或少数社区、学校、医院等抽取受试者的便利样本。这方面的一个典型例子是弗雷明汉心脏研究 (FHS) ）。此类样本的许多局限性是众所周知的，但之前尚未解决由于社交网络关系导致的统计依赖性问题。我们表明，连同反保守方差估计，由于网络依赖性，这可能导致虚假关联. 使用我们根据空间自相关开发的统计测试改编的统计测试，我们在使用 FHS 数据发表的数千篇有影响力的论文中的几篇中测试了网络依赖性。结果表明，使用 FHS 数据对冠心病、其他健康结果和同伴影响进行了数十年的研究，其中一些研究可能会因未确认的网络依赖性而遭受虚假关联、容易出错的点估计和反保守推断。这些问题并非 FHS 独有；随着心理学、医学等领域的研究人员努力应对复制失败，这种未被承认的无效统计推断来源应该成为对话的一部分。本文的补充材料，包括对可用于复制作品的材料的标准化描述，