title: A new approach to categorising continuous variables in prediction models: Proposal and validation.
abstract: When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2was categorised in a univariable and a multivariable setting.
authors.affiliation: 1 Departamento de Matemática Aplicada, Estadística e Investigación Operativa, Universidad del País Vasco UPV/EHU, Leioa, Spain.|2 Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Galdakao, Spain.
authors.forename: Irantzu
authors.initials: I
authors.lastname: Barrio
authors.affiliation: 1 Departamento de Matemática Aplicada, Estadística e Investigación Operativa, Universidad del País Vasco UPV/EHU, Leioa, Spain.|2 Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Galdakao, Spain.|3 BCAM - Basque Center for Applied Mathematics, Bilbao, Spain.
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Arostegui
authors.affiliation: 4 Departamento de Estadística e Investigación Operativa. Universidade de Vigo, Vigo, Spain.
authors.forename: María-Xosé
authors.initials: MX
authors.lastname: Rodríguez-Álvarez
authors.affiliation: 2 Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Galdakao, Spain.|5 Unidad de Investigación, Hospital Galdakao-Usansolo, Galdakao, Spain.
authors.forename: José-María
authors.initials: JM
authors.lastname: Quintana
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D056808:Biostatistics / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology / Q000628:therapy; D018401:Sample Size; D012720:Severity of Illness Index; D012984:Software
keywords: Categorisation; cut point; prediction models; validation