Tutorial on Multiple Mediation Analysis Using Causal Networks: Application to Diagnosing COVID-19 From Its Early and...

There are two methods of studying multiple mediation: network-based and analysis of coefficients in regression equations.

This tutorial shows how multiple mediation analysis can be conducted through first constructing causal networks; and then evaluating the direct and mediated impact within the network. The proposed method is demonstrated in the context of diagnosing COVID-19 from its symptoms.

822 individuals who had completed a COVID-19 test were recruited through listservs and via employees and patients of Virginia Commonwealth University Health Center. Participants reported their symptoms and which symptom(s) occurred first. A Causal Network model was established through a repeated chain of regressions in four steps: First, we identified the order of occurrence of symptoms. Second, COVID-19 test results were LASSO regressed on symptoms and demographic variables, establishing direct effects. Third, the direct effects were LASSO regressed on prior symptoms and demographic variables, establishing indirect effects. Fourth, the joint distribution of the variables in the network was simulated by evaluating regression equations at factorial combinations of their direct effects. Fifth, the mediated effect was calculated through twin modeling, where the model derived from the real data was compared to the counterfactual model that represented 'what if' there was no mediation.

The 10-fold cross-validated area under the receiver curve for the network model was 0.82, which is a moderate to high level of accuracy. The network model identified later symptoms (e.g., chills) mediated the effect of earlier symptoms (e.g. fever).

A network-based multiple mediation analysis led to new insights by integrating findings of 19 separate regressions into a single network model. The procedure showed how artificial intelligence can help in triage of COVID-19 patients from their symptoms, before any home or laboratory tests.