Explain/Predict in Epidemiology

Researchers in various fields have been sending me emails and reactions after reading my 2010 paper "To Explain or To Predict?". While I am aware of research methodology in a few areas, I'm learning in more detail about the scientific challenges caused by "predictive-less" areas.

In an effort to further disseminate this knowledge, I'll be posting these reactions in this blog (with the senders' approval, of course).

In a recent email, Stan Young, Assistant Director for Bioinformatics at NISS, commented about the explain/predict situation in epidemiology:

"I enjoyed reading your paper... I am interested in what I think is [epidemiologists] lack of clarity on explain/predict. They seem to take the position that no matter how many tests they compute, that any p-value <0.05 is a strong indication of something real (=explain) and that everyone should follow their policies (=predict) when, given all their analysis problems, they at the very best should consider their claims as hypothesis generating."

In a talk by epidemiology Professor Uri Goldbourt, who was a discussant in a recent "Explain or Predict" panel, I learned that modeling in epidemiology is nearly entirely descriptive. Unlike explanatory modeling, there is little underlying causal theory. And there is no prediction or evaluation of predictive power going on. Modeling typically focuses on finding correlations between measurable variables in observational studies that generalize to the population (and hence the wide use of inference, and unfortunately, a huge issue of multiple testing).

Predictive modeling has a huge potential to advance research in epidemiology. Among many benefits (such as theory validation), it would bring the field closer to today's "personalized" environment. Not only concentrating on "average patterns", but also generating personalized predictions for individuals.

I'd love to hear more from epidemiologists! Please feel free to post comments or to email me directly.