Collins, Gary S. and Ogundimu, Emmanuel O. and Cook, Jonathan A. and Manach, Yannick Le and Altman, Douglas G. (2016) 'Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model.', Statistics in Medicine, 35 (23). pp. 4124-4135.
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.
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|Publisher Web site:||https://doi.org/10.1002/sim.6986|
|Publisher statement:||© 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.|
|Date accepted:||22 April 2016|
|Date deposited:||15 October 2021|
|Date of first online publication:||18 May 2016|
|Date first made open access:||15 October 2021|
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