2014-08-30
Net reclassification improvement and integrated discrimination improvement require calibrated models: Relevance from a marker and model perspective
Publication
Publication
Statistics in Medicine , Volume 33 - Issue 19 p. 3415- 3418
Introduction
For the last three decades, clinical prediction models have mainly been evaluated on the basis of their
ability to discriminate between persons who develop the event of interest and persons who do not, as
quantified by the c-statistic or area under the receiver operator characteristic curve (AUC). The
AUC considers sensitivity and specificity of the model over all possible cut-points of predicted risk.
However, prediction models are often used to classify patients into risk categories that correspond to
diagnostic or therapeutic decisions. This provoked the idea of comparing models according to their
ability to adequately assign clinical risk categories based on absolute risk estimates. Analyses of
risk reclassification have hit the ground running: uptake of measures such as net reclassification
improvement (NRI) has been enormous, and guidance documents on evaluations of markers and
prediction models embraced it as a step prior to full-blown cost-effectiveness analysis.More recently,
several researchers reviewed the current applications of reclassification analysis and expressed concerns
about inappropriate use.
Additional Metadata | |
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doi.org/10.1002/sim.6133, hdl.handle.net/1765/53045 | |
Statistics in Medicine | |
Organisation | Erasmus MC: University Medical Center Rotterdam |
Leening, M., Steyerberg, E., Van Calster, B., D'Agostino, R., & Pencina, M. (2014). Net reclassification improvement and integrated discrimination improvement require calibrated models: Relevance from a marker and model perspective. Statistics in Medicine (Vol. 33, pp. 3415–3418). doi:10.1002/sim.6133 |