Why most studies into COVID19 risk factors may be producing flawed conclusions - and how to fix the problem

 

In a new paper we extend the recent work by Griffith et al which 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 over-represented. For example, collider bias caused by smokers being under-represented in the dataset may (at least partly) explain recent empirical results that suggest smoking reduces the risk of COVID19.

The new paper makes more explicit use of graphical causal models to interpret observed data. We show that the Griffith et al 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, exactly this has been claimed for hypertension and the same data could even - bizarrely - find factors like 'being in close contact with COVID19 patients' reducing the risk of COVID19. To avoid such 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.

The paper is here:

Fenton, N E (2020)  "Why most studies into COVID19 risk factors may be producing flawed conclusions - and how to fix the problem",  arxiv.org/abs/2005.08608  (pdf is also available here)

 See also:

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  Simpson's Paradox Example 2: Food and exercise