A new concordance measure for risk prediction models in external validation settings.

ABSTRACT: Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression co

SUBMITTER: van Klaveren D 

PROVIDER: S-EPMC5550798 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature