Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model
| Autor: | Gary S. Collins, Jonathan Cook, Douglas G. Altman, Emmanuel O. Ogundimu, Yannick Le Manach |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Epidemiology Calibration (statistics) Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Econometrics Humans Medicine 030212 general & internal medicine Research Articles Prognostic models Potential impact Models Statistical business.industry G300 Prognosis Medical statistics continuous predictors Outcome (probability) 3. Good health B900 Linear relationship 030220 oncology & carcinogenesis Prognostic model Restricted cubic splines Artificial intelligence prognostic modelling dichotomisation business computer Algorithms Research Article |
| Zdroj: | Statistics in Medicine, 2016, Vol.35(23), pp.4124-4135 [Peer Reviewed Journal] Statistics in Medicine |
| ISSN: | 1097-0258 0277-6715 |
| DOI: | 10.1002/sim.6986 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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