Classical regression and predictive modeling
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Date
2022-05
Authors
Cook, Richard
Lee, Ker-Ai
Lo, Benjamin W.Y.
Macdonald, R. Loch
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
BACKGROUND: With the advent of personalized and stratified medicine, there has been much discussion about predictive modeling and the role of classical regression in modern medical research. We describe and distinguish the goals in these two frameworks for analysis.
METHODS: The assumptions underlying and utility of classical regression are reviewed for continuous and binary outcomes. The tenets of predictive modeling are then discussed and contrasted. Principles are illustrated by simulation and through application of methods to a neurosurgical study.
RESULTS: Classical regression can be used for insights into causal mechanisms if careful thought is given to the role of variables of interest and potential confounders. In predictive modeling, interest lies more in accuracy of predictions and so alternative metrics are used to judge adequacy of models and methods; methods which average predictions over several contending models can improve predictive performance but these do not admit a single risk score.
CONCLUSIONS: Both classical regression and predictive modeling have important roles in modern medical research. Understanding the distinction between the two framework for analysis is important to place them in their appropriate context and interpreting findings from published studies appropriately.
Description
The final publication of “Cook RJ, Lee K-A, Lo BWY, Macdonald RL (2022). Classical regression and predictive modeling. World Neurosurgery, 161, 251-264” is available at Elsevier via https://doi.org/10.1016/j.wneu.2022.02.030 @ 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
Keywords
assocation, causal analysis, explained variation, classification, prediction, predictive accuracy