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Luo, Jingqin; Xiong, Chengjie

Youden index and associated cut-points for three ordinal diagnostic groups. (English) Zbl 1347.62040

Commun. Stat., Simulation Comput. 42, No. 6, 1213-1234 (2013).
Summary: Directly relating to sensitivity and specificity and providing an optimal cut-point, which maximizes overall classification effectiveness for diagnosis purpose, the Youden index has been frequently utilized in biomedical diagnosis practice. Current application of the Youden index is limited to two diagnostic groups. However, there usually exists a transitional intermediate stage in many disease processes. Early recognition of this intermediate stage is vital to open an optimal window for therapeutic intervention. In this article, we extend the Youden index to assess diagnostic accuracy when there are three ordinal diagnostic groups. Parametric and nonparametric methods are presented to estimate the optimal Youden index, the underlying optimal cut-points, and the associated confidence intervals. Extensive simulation studies covering representative distributional assumptions are reported to compare performance of the proposed methods. A real example illustrates the usefulness of the Youden index in evaluating discriminating ability of diagnostic tests.

MSC:

62F10 Point estimation
62F12 Asymptotic properties of parametric estimators
62F25 Parametric tolerance and confidence regions
62F40 Bootstrap, jackknife and other resampling methods

Keywords:

bandwidth selection; diagnostic test; kernel smoothing; optimal cut-point; Youden index

Software:

DiagTest3Grp
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\textit{J. Luo} and \textit{C. Xiong}, Commun. Stat., Simulation Comput. 42, No. 6, 1213--1234 (2013; Zbl 1347.62040)
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References:

[1] DOI: 10.1137/0916069 · Zbl 0836.65080
[2] DOI: 10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F
[3] DOI: 10.1007/BF02124750 · Zbl 0863.65008
[4] DOI: 10.1007/978-3-540-39857-8_12
[5] DOI: 10.1002/bimj.200410135
[6] DOI: 10.1016/0022-1759(95)00121-P
[7] DOI: 10.1016/S0167-5877(00)00115-X
[8] DOI: 10.1002/(SICI)1097-0258(19960530)15:10<969::AID-SIM211>3.0.CO;2-9
[9] Jingqin Luo, Journal of Statistical Software (2012)
[10] Kitaharaa F., Gastrointestinal Cancer 44 pp 693– (1999)
[11] DOI: 10.1016/j.patrec.2007.05.001
[12] DOI: 10.1016/j.jspi.2009.05.043 · Zbl 1206.62053
[13] DOI: 10.1214/aos/1018031201 · Zbl 0938.62035
[14] DOI: 10.1177/0272989X9901900110
[15] DOI: 10.1002/sim.1917
[16] Patel J. K., Handbook of the Normal Distribution. Vol. 150 (1996) · Zbl 0846.62010
[17] DOI: 10.1002/bimj.200410133
[18] DOI: 10.1093/aje/kwj063
[19] Puri M. L., Nonparametric Methods in Multivariate Analysis (1971) · Zbl 0237.62033
[20] DOI: 10.1093/biomet/90.3.585 · Zbl 1436.62620
[21] DOI: 10.1080/03610910701212181 · Zbl 1121.62101
[22] Sheather S. J., Computational Statistics 7 pp 225– (1992)
[23] Sheather S. J., Journal of Royal Statistical Society Series B 53 pp 683– (1991)
[24] Silverman B. W., Density Estimation for Statistics and Data Analysis (1986) · Zbl 0617.62042
[25] DOI: 10.1111/j.1541-0420.2007.00941.x · Zbl 1145.62391
[26] DOI: 10.1002/sim.3035
[27] Wasserman L., All of Statistics: A Concise Course in Statistical Inference (2005) · Zbl 1053.62005
[28] DOI: 10.1002/sim.2433
[29] DOI: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
[30] DOI: 10.1002/sim.1011
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