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Statistical methods for the estimation of contagion effects in human disease and health networks.
Computational and Structural Biotechnology Journal ( IF 6 ) Pub Date : 2020-06-25 , DOI: 10.1016/j.csbj.2020.06.027
Ran Xu 1
Affiliation  

Contagion effects, sometimes referred to as spillover or influence effects, have long been central to the study of human disease and health networks. Accurate estimation and identification of contagion effects are important in terms of understanding the spread of human disease and health behavior, and they also have various implications for designing effective public health interventions. However, many challenges remain in estimating contagion effects and it is often unclear when it is difficult to correctly estimate contagion effects, or why a particular method would need to be applied. In this review we explain the challenges in estimating contagion effects, and how they can be framed as an omitted variable bias problem. We then discuss how such challenges have been addressed in randomized experiments and traditional statistical analyses, as well as several state-of-the-art statistical methods. Finally, we conclude by summarizing recent advancements and noting remaining challenges, as well as appropriate next steps.



中文翻译:

估计人类疾病和健康网络中传染效应的统计方法。

传染效应,有时被称为溢出效应或影响效应,长期以来一直是人类疾病和健康网络研究的中心。就了解人类疾病的传播和健康行为而言,准确估计和识别传染病影响很重要,并且它们对设计有效的公共卫生干预措施也有多种含义。然而,在估计传染效应方面仍存在许多挑战,通常尚不清楚何时难以正确估计传染效应,或为什么需要采用特定方法。在这篇综述中,我们解释了估计传染效应的挑战,以及如何将其构造为忽略的变量偏差问题。然后,我们讨论如何在随机实验和传统的统计分析中解决此类挑战,以及几种最先进的统计方法。最后,我们通过总结最新进展并指出尚存的挑战以及适当的后续步骤作为总结。

更新日期:2020-06-25
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