Biomarkers of Heart Health Don’t Improve Our Predictions of Heart Disease

Understanding why strong associations don’t always equal strong predictions.

It’s the counterintuitive stuff in epidemiology that always really interests me. One intuition many of us have is that if a risk factor is significantly associated with an outcome, knowledge of that risk factor would help to predict that outcome. Makes sense. Feels right.

But it’s not right. Not always.

A fake example to illustrate my point. Let’s say we have 10,000 individuals who we follow for 10 years — and 2000 of them die — it’s been a rough decade. At baseline, I had measured a novel biomarker — the Perry Factor — in everyone. To keep it simple, the Perry Factor has only two values, 0 or 1.

I then do a standard associational analysis — and find this. Individuals who are positive for the Perry Factor have a 40-fold higher odds of death than those who are negative for it. I am beginning to reconsider ascribing my good name to this biomarker. This is a highly statistically significant result — a p-value less than 0.001.