Evaluation of polygenic risk models using multiple performance measures: a critical assessment of discordant results

Forike K. Martens, Elisa C. M. Tonk, A. Cecile J. W. Janssens

Research output: Contribution to journalArticleAcademicpeer-review


Purpose: The area under the receiver operating characteristic curve (AUC) is commonly used for evaluating the improvement of polygenic risk models and increasingly assessed together with the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We evaluated how researchers described and interpreted AUC, NRI, and IDI when simultaneously assessed. Methods: We reviewed how researchers described definitions of AUC, NRI, and IDI and how they computed each metric. Next, we reviewed how the increment in AUC, NRI, and IDI were interpreted, and how the overall conclusion about the improvement of the risk model was reached. Results: AUC, NRI, and IDI were correctly defined in 63, 70, and 0% of the articles. All statistically significant values and almost half of the nonsignificant were interpreted as indicative of improvement, irrespective of the values of the metrics. Also, small, nonsignificant changes in the AUC were interpreted as indication of improvement when NRI and IDI were statistically significant. Conclusion: Researchers have insufficient knowledge about how to interpret the various metrics for the assessment of the predictive performance of polygenic risk models and rely on the statistical significance for their interpretation. A better understanding is needed to achieve more meaningful interpretation of polygenic prediction studies.
Original languageEnglish
Pages (from-to)391-397
JournalGenetics in Medicine
Issue number2
Early online date12 Jun 2018
Publication statusPublished - 1 Feb 2019

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