Autor/es de la URV: Arostegui, Inmaculada Esteban, Cristóbal Meira-Machado, Luis Rodríguez-Álvarez, María Xosé Barrio, Irantzu

Palabras clave: Categorisation, prediction models, cutpoint, Cox model

Resumen: The Cox proportional hazards model is the most widely used survival prediction model for analysing time-to-event data. To measure the discrimination ability of a survival model the concordance probability index is widely used. In this work we studied and compared the performance of two different estimators of the concordance probability when a continuous predictor variable is categorised in a Cox proportional hazards regression model. In particular, we compared the c-index and the concordance probability estimator. We evaluated the empirical performance of both estimators through simulations. To categorise the predictor variable we propose a methodology which considers the maximal discrimination attained for the categorical variable. We applied this methodology to a cohort of patients with chronic obstructive pulmonary disease, in particular, we categorised the predictor variable forced expiratory volume in one second in percentage.

Año de publicación de la revista: 2017

Tipo de publicación: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article