Inference and Prediction Diverge in Biomedicine.
Where this comes from
- Record sourced from PubMed, PMID 33294865.
- Also identified by DOI 10.1016/j.patter.2020.100119 and PMC identifier 7691397.
- Licence recorded as CC BY-NC-ND.
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Abstract
In the 20<sup>th</sup> century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. In the early 21<sup>st</sup> century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future. Our comparison applies linear models for identifying significant contributing variables and for finding the most predictive variable sets. In systematic data simulations and common medical datasets, we explored how variables identified as significantly relevant and variables identified as predictively relevant can agree or diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships, but not vice versa. More complete understanding of different ways to define "important" associations is a prerequisite for reproducible research and advances toward personalizing medical care.