Article

Performance of reclassification statistics in comparing risk prediction models

Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Biometrical Journal (Impact Factor: 1.24). 03/2011; 53(2):237-58. DOI: 10.1002/bimj.201000078
Source: PubMed

ABSTRACT Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information.

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    • "Models were tested using the c-index improvement, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) according to recently suggested risk limits 0% to <5%, 5% to <10%, 10% to <20% and ≥20% for 10 year risk limits [29]. In addition, ‘Clinical’ NRI quantifies the improvement in prediction in the intermediate risk group (5–20%) which incorporates a correction for the expected value of improvement [30]. This test measures the reclassification where only individuals in the intermediate group are tested with the GRS and have their risk recalculated. "
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    • "The clinical utility of risk models can also be estimated by measures of reclassification. The net reclassification index (NRI) and the integrated discrimination improvement (IDI) assess whether a novel marker can improve the risk classification for individuals, as compared with models without the marker of interest (Cook & Paynter, 2011; Steyerberg et al. 2010). Studies incorporating such analyses will also be needed to determine whether a genetic risk score provides any additional clinical utility "
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