Classical Regression and Predictive Modeling

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 2 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 2 frameworks for analysis is important to place them in their appropriate context and interpreting findings from published studies appropriately.

Keywords: Association; Causal analysis; Classification; Explained variation; Prediction; Predictive accuracy.