Evaluation of the overall accuracy of biomarkers might be based on average measures of the sensitivity for all possible specificities -and vice versa- or equivalently the area under the receiver operating characteristic (ROC) curve that is typically used in such settings. In practice clinicians are in need of a cutoff point to determine whether intervention is required after establishing the utility of a continuous biomarker. The Youden index can serve both purposes as an overall index of a biomarker's accuracy, that also corresponds to an optimal, in terms of maximizing the Youden index, cutoff point that in turn can be utilized for decision making. In this paper, we provide new methods for constructing confidence intervals for both the Youden index and its corresponding cutoff point. We explore approaches based on the delta approximation under the normality assumption, as well as power transformations to normality and nonparametric kernel- and spline-based approaches. We compare our methods to existing techniques through simulations in terms of coverage and width. We then apply the proposed methods to serum-based markers of a prospective observational study involving diagnosis of late-onset sepsis in neonates.
Bibliographical noteFunding Information:
The research of Prof. Benjamin Reiser was supported by the Israel Science Foundation (grant No. 387/15). We would like to thank Dr. Sarafidis for providing the LOS data discussed in the application. The authors would also like to thank two anonymous referees and the associate editor for their comments and insights that significantly improved this paper.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
- Box-Cox transformation
- ROC curve
- Youden index
- delta method
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty