Time-Dependent ROC Curve for Multiple Longitudinal Biomarkers and Its Application in Diagnosing Cardiovascular Events.
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| Title: | Time-Dependent ROC Curve for Multiple Longitudinal Biomarkers and Its Application in Diagnosing Cardiovascular Events. |
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| Authors: | Sun L; School of Statistics, Shanxi University of Finance and Economics, Taiyuan, Shanxi, People's Republic of China.; Beijing International Center for Mathematical Research, Peking University, Beijing, People's Republic of China., Wei P; Beijing International Center for Mathematical Research, Peking University, Beijing, People's Republic of China., Zhou J; School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China., Zhou XH; Beijing International Center for Mathematical Research, Peking University, Beijing, People's Republic of China. |
| Source: | Statistics in medicine [Stat Med] 2025 Feb 28; Vol. 44 (5), pp. e10318. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | 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- |
| MeSH Terms: | Biomarkers*/blood , Biomarkers*/analysis , Cardiovascular Diseases*/diagnosis , Cardiovascular Diseases*/blood , ROC Curve*, Humans ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/blood ; Longitudinal Studies ; Glycated Hemoglobin/analysis ; Logistic Models ; Cholesterol, LDL/blood ; Time Factors ; Computer Simulation |
| Abstract: | Since they can help people detect the early signs of diseases, accurate diagnostic techniques based on biomarkers are crucial in biomedical research. This article proposes a novel bivariate time-varying coefficients logistic regression model for addressing the combined longitudinal biomarkers. Using the B-splines method to estimate the proposed model, we can effectively combine multiple longitudinal biomarkers and improve diagnostic accuracy. We show that the proposed method is theoretically consistent. And it exhibits superior performance compared to the existing method, as presented through numerical results. The proposed method is verified in a study on predicting the probability of onset of future cardiovascular events for type 2 diabetic patients. The longitudinal biomarkers, HbA1c and LDL-C, are considered in this study. We demonstrate that the combined longitudinal biomarkers significantly improved disease diagnostic accuracy over only a combination of the latest measured biomarkers in most cases. (© 2025 John Wiley & Sons Ltd.) |
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| Grant Information: | JWZQ20240101027 Beijing Natural Science Foundation; 12171329 National Natural Science Foundation of China; 82173623 National Natural Science Foundation of China; Novo Nordisk |
| Contributed Indexing: | Keywords: biomarker combination; longitudinal data; real‐time diagnosis; time‐varying model |
| Substance Nomenclature: | 0 (Biomarkers) 0 (Glycated Hemoglobin) 0 (Cholesterol, LDL) 0 (hemoglobin A1c protein, human) |
| Entry Date(s): | Date Created: 20250206 Date Completed: 20250506 Latest Revision: 20250506 |
| Update Code: | 20250506 |
| DOI: | 10.1002/sim.10318 |
| PMID: | 39912243 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Since they can help people detect the early signs of diseases, accurate diagnostic techniques based on biomarkers are crucial in biomedical research. This article proposes a novel bivariate time-varying coefficients logistic regression model for addressing the combined longitudinal biomarkers. Using the B-splines method to estimate the proposed model, we can effectively combine multiple longitudinal biomarkers and improve diagnostic accuracy. We show that the proposed method is theoretically consistent. And it exhibits superior performance compared to the existing method, as presented through numerical results. The proposed method is verified in a study on predicting the probability of onset of future cardiovascular events for type 2 diabetic patients. The longitudinal biomarkers, HbA1c and LDL-C, are considered in this study. We demonstrate that the combined longitudinal biomarkers significantly improved disease diagnostic accuracy over only a combination of the latest measured biomarkers in most cases.<br /> (© 2025 John Wiley & Sons Ltd.) |
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| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.10318 |