Abstract

In data analysis, age is usually considered a potential confounder and the reflex is to control for it, for example as a covariate in a regression model or as a stratifying factor.

Health research focusing on longevity investigates how a healthy state can be maintained or extended as we age. It makes a distinction between chronological age, measuring rotations of the Earth around itself and around the Sun, and biological age, a loosely defined concept with different manifestations – the ability to divide and repair tissues, the production of proteins supporting tissue structure, the ability to eliminate toxins, mitochondrial health, or mutation load.

A tenet for healthy aging research is that biological age can be influenced unlike chronological age. For data analysis, statistics, or causal interpretation of machine learning models, this means that the default recommendation to control for age as a potential confounder may no longer apply when biological age is involved. Biological age can also be a mediator, or a collider. When the latter, the variable must not be controlled in the analysis.