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

Gary Collins, Emmanuel Ogundimu, Jonathan Cook, Yannick Le Manach, Douglas Altman

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)
13 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)4124-4135
JournalStatistics in Medicine
Volume35
Issue number23
Early online date18 May 2016
DOIs
Publication statusPublished - 15 Oct 2016
Externally publishedYes

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