Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model

Collins, Gary S.; Ogundimu, Emmanuel O.; Cook, Jonathan A.; Manach, Yannick Le; Altman, Douglas G.

Abstract

Continuous predictors are routinely encountered when developing a prognostic model. Investigators, who are often non-statisticians, must decide how to handle continuous predictors in their models. Categorising continuous measurements into two or more categories has been widely discredited, yet is still frequently done because of its simplicity, investigator ignorance of the potential impact and of suitable alternatives, or to facilitate model uptake. We examine three broad approaches for handling continuous predictors on the performance of a prognostic model, including various methods of categorising predictors, modelling a linear relationship between the predictor and outcome and modelling a nonlinear relationship using fractional polynomials or restricted cubic splines. We compare the performance (measured by the c-index, calibration and net benefit) of prognostic models built using each approach, evaluating them using separate data from that used to build them. We show that categorising continuous predictors produces models with poor predictive performance and poor clinical usefulness. Categorising continuous predictors is unnecessary, biologically implausible and inefficient and should not be used in prognostic model development. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Citation

Collins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., & Altman, D. G. (2016). Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Statistics in Medicine, 35(23), 4124-4135. https://doi.org/10.1002/sim.6986

• Journal Article Type: Article
• Acceptance Date: Apr 22, 2016
• Online Publication Date: May 18, 2016
• Publication Date: Oct 15, 2016
• Deposit Date: Oct 11, 2020
• Publicly Available Date: Oct 15, 2021
• Journal: Statistics in Medicine
• Print ISSN: 0277-6715
• Electronic ISSN: 1097-0258
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 23
• Pages: 4124-4135
• DOI: https://doi.org/10.1002/sim.6986
• Public URL: https://durham-repository.worktribe.com/output/1260427