Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2017

Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies

  • Identification data

    Identifier: RP:2455
    Handle: https://hdl.handle.net/20.500.11797/RP2455
    Authors:
    Arostegui, InmaculadaEsteban, CristóbalMeira-Machado, LuisRodríguez-Álvarez, María XoséBarrio, Irantzu
    Abstract:
    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.

  • Others:

    URV's Author/s: Arostegui, Inmaculada Esteban, Cristóbal Meira-Machado, Luis Rodríguez-Álvarez, María Xosé Barrio, Irantzu
    Keywords: Categorisation, prediction models, cutpoint, Cox model
    Abstract: 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.
    Journal publication year: 2017
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Categorisation, prediction models, cutpoint, Cox model

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