Journal
PATTERNS
Volume 1, Issue 8, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.patter.2020.100119
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Funding
- National Institutes of Health (NIH) [R01 AG068563A]
- Canadian Institutes of Health Research (CIHR) [438531]
- Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund)
- Google (Research/Teaching Award)
- CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research)
- Deutsche Forschungsgemeinschaft (DFG) [BZ2/2-1, BZ2/3-1, BZ2/4-1, IRTG2150]
- Amazon AWS Research Grant
- German National Merit Foundation
- START-Programof the Faculty of Medicine [126/16]
- Exploratory Research Space, RWTH Aachen [OPSF449]
- European Union [604102]
- French National Institute for Informatics and Automation (INRIA)
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In the 20th century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. In the early 21st 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.
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