Biomarker Combination Based on the Youden Index With and Without Gold Standard.
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| Názov: | Biomarker Combination Based on the Youden Index With and Without Gold Standard. |
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| Autori: | Sun A; Center of Data Science, Peking University, Beijing, China., Li Y; Rheumatology Department, Guang'anmen Hospital, Beijing, China., Zhou XH; Department of Biostatistics and Beijing International Center for Mathematical Research, Peking University, Beijing, China. |
| Zdroj: | Statistics in medicine [Stat Med] 2025 Aug; Vol. 44 (18-19), pp. e70189. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Výrazy zo slovníka MeSH: | Biomarkers*/analysis , ROC Curve*, Humans ; Area Under Curve ; Computer Simulation ; Models, Statistical ; Medicine, Chinese Traditional ; Reference Standards |
| Abstrakt: | In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the area under the ROC curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. However, practical diagnostic settings require both optimal combination coefficients and an effective cutoff value, and the reference test may be imperfect. In this article, we propose a two-stage method for identifying the optimal linear combination and cutoff value based on the Youden index. First, it maximizes an approximation of the empirical AUC to estimate the optimal linear coefficients for combining multiple biomarkers. Then, it maximizes the empirical Youden index to determine the optimal cutoff point for disease classification. Under the semiparametric single index model and regularity conditions, the estimators for the linear coefficients, cutoff point, and Youden index are consistent. This method is also applicable when the reference standard is imperfect. We demonstrate the performance of our method through simulations and apply it to construct a diagnostic scale for Chinese medicine. (© 2025 John Wiley & Sons Ltd.) |
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| Grant Information: | 82173623 National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: Chinese medicine scales; Youden index; imperfect reference test; linear combination |
| Substance Nomenclature: | 0 (Biomarkers) |
| Entry Date(s): | Date Created: 20250819 Date Completed: 20250826 Latest Revision: 20250826 |
| Update Code: | 20250826 |
| DOI: | 10.1002/sim.70189 |
| PMID: | 40827373 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the area under the ROC curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. However, practical diagnostic settings require both optimal combination coefficients and an effective cutoff value, and the reference test may be imperfect. In this article, we propose a two-stage method for identifying the optimal linear combination and cutoff value based on the Youden index. First, it maximizes an approximation of the empirical AUC to estimate the optimal linear coefficients for combining multiple biomarkers. Then, it maximizes the empirical Youden index to determine the optimal cutoff point for disease classification. Under the semiparametric single index model and regularity conditions, the estimators for the linear coefficients, cutoff point, and Youden index are consistent. This method is also applicable when the reference standard is imperfect. We demonstrate the performance of our method through simulations and apply it to construct a diagnostic scale for Chinese medicine.<br /> (© 2025 John Wiley & Sons Ltd.) |
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| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.70189 |