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You say
"Andrew and Lauren suggest that the practice of using regression models without interactions is due to lack of data. It takes "16 times the sample size" to estimate one interaction effect relative to the main effect.

I get it when the observational data came from the professor's undergraduate class of 100 students but in Covid-19 studies, they have easily hundreds of thousands, if not millions, of subjects."

I get your point, but in reality those millions of data points are rarely available (especially for health data). In theory, the data exists, but any of the data sets I have available to me are much smaller than I like - and the inclusion of interaction effects is severely limited by that. With health data in particular, you would think interaction effects are especially important, so an unintended consequence of HIPAA is to limit the scope of interaction effects that can be included in observational studies.


Dale: Fair point. I may have a warped perspective as I've been reading Covid-vaccine-related studies and those seem to have access to government databases for the whole population.

In the paper, I also like this proposal: "When moving to analysis, interactions are important but hard to estimate with precision. So when we do include interactions in our model, we should estimate them using regularization and not demand that they attain statistical significance or any other threshold representing near-certainty."

Perhaps not recognized enough by practitioners is the fact that "not including interactions" is equivalent to "assuming no interactions".

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