关于“对撞机偏差破坏了我们对COVID-19疾病风险和严重程度的理解”以及因果贝叶斯网络如何暴露和解决问题的说明
A note on 'Collider bias undermines our understanding of COVID-19 disease risk and severity' and how causal Bayesian networks both expose and resolve the problem
摘要：
Griffith等人最近的一份重要预印本强调了COVID 19研究中的“碰撞机偏见”如何破坏了我们对疾病风险和严重程度的理解。这通常是由于数据仅限于接受COVID 19检测的人，其中医疗保健工作者的比例过高。例如，由吸烟者在数据集中代表性不足引起的碰撞机偏差可以（至少部分地）解释表明吸烟降低COVID风险的经验结果。我们扩展了Griffith等人的工作，更明确地使用图形因果模型来解释观测数据。我们表明，他们的吸烟的例子可以澄清和改进，使用贝叶斯网络模型与现实的数据和假设。我们发现，像“压力”这样的风险因素存在一个更根本的问题，与吸烟不同，它在医疗保健工作者中更普遍而不是更不普遍;在这种情况下，由于来自有偏见的数据集的碰撞器偏见和“医疗保健工作者”是一个混淆变量的事实，研究很可能会错误地得出结论，压力降低而不是增加COVID 19的风险。事实上，“与COVID 19人群密切接触”降低了COVID 19的风险。为了避免这种潜在的错误结论，任何对观测数据的分析都必须考虑到潜在的因果结构，包括碰撞和混杂因素。如果分析师未能明确做到这一点，那么他们就特定风险因素对COVID 19的影响所做的任何结论都可能存在缺陷。
英文摘要：
An important recent preprint by Griffith et al highlights how 'collider bias' in studies of COVID19 undermines our understanding of the disease risk and severity. This is typically caused by the data being restricted to people who have undergone COVID19 testing, among whom healthcare workers are overrepresented. For example, collider bias caused by smokers being underrepresented in the dataset may (at least partly) explain empirical results that suggest smoking reduces the risk of COVID19. We extend the work of Griffith et al making more explicit use of graphical causal models to interpret observed data. We show that their smoking example can be clarified and improved using Bayesian network models with realistic data and assumptions. We show that there is an even more fundamental problem for risk factors like 'stress' which, unlike smoking, is more rather than less prevalent among healthcare workers; in this case, because of a combination of collider bias from the biased dataset and the fact that 'healthcare worker' is a confounding variable, it is likely that studies will wrongly conclude that stress reduces rather than increases the risk of COVID19. Indeed, "being in close contact with COVID19 people" reduces the risk of COVID19. To avoid such potentially erroneous conclusions, any analysis of observational data must take account of the underlying causal structure including colliders and confounders. If analysts fail to do this explicitly then any conclusions they make about the effect of specific risk factors on COVID19 are likely to be flawed.
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