Functional covariate-adjusted partial area under the specificity-ROC curve with an application to metabolic syndrome diagnosis
| Autor: | W. González-Manteiga, T. A. Alonzo, Miguel de Carvalho, Vanda Inacio de Carvalho |
|---|---|
| Přispěvatelé: | Universidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización |
| Rok vydání: | 2016 |
| Předmět: |
average specificity
0301 basic medicine Statistics and Probability Functional covariate-adjustment specificity-receiver operating characteristic curve Kernel regression 01 natural sciences functional covariate-adjustment metabolic syndrome Gamma-glutamyl transferase 010104 statistics & probability 03 medical and health sciences Sensitivity Arterial oxygen saturation Statistics Covariate Sensitivity (control systems) 0101 mathematics Medical diagnosis Mathematics Receiver operating characteristic partial area under the curve Partial area under the curve Area under the curve Nonparametric statistics Estimator Biomarker sensitivity Metabolic syndrome 030104 developmental biology Average specificity kernel regression Modeling and Simulation biomarker gamma-glutamyl transferase Statistics Probability and Uncertainty Specificity-receiver operating characteristic curve |
| Zdroj: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela instname Calhau Fernandes Inacio De Carvalho, V, de Carvalho, M, Alonzo, T A & González-Manteiga, W 2016, ' Functional Covariate-Adjusted Partial Area under the Specificity-ROC Curve with an Application to Metabolic Syndrome Diagnosis ', Annals of Applied Statistics, vol. 10, no. 3, pp. 1472-1495 . https://doi.org/10.1214/16-AOAS943 Ann. Appl. Stat. 10, no. 3 (2016), 1472-1495 |
| ISSN: | 1932-6157 |
| DOI: | 10.1214/16-aoas943 |
| Popis: | Due to recent advances in technology, medical diagnosis data are becoming increasingly complex and, nowadays, applications where measurements are curves or images are ubiquitous. Motivated by the need of modeling a functional covariate on a metabolic syndrome case study, we develop a nonparametric functional regression model for the area under the specificity receiver operating characteristic curve. This partial area is a meaningful summary measure of diagnostic accuracy for cases in which misdiagnosis of diseased subjects may lead to serious clinical consequences, and hence it is critical to maintain a high sensitivity. Its normalized value can be interpreted as the average specificity over the interval of sensitivities considered, thus summarizing the trade-off between sensitivity and specificity. Our methods are motivated by, and applied to, a metabolic syndrome study that investigates how restricting the sensitivity of the gamma-glutamyl-transferase, a metabolic syndrome marker, to certain clinical meaningful values, affects its corresponding specificity and how it might change for different curves of arterial oxygen saturation. Application of our methods suggests that oxygen saturation is key to gamma-glutamyl transferase’s performance and that some of the different intervals of sensitivities considered offer a good tradeoff between sensitivity and specificity. The simulation study shows that the estimator associated with our model is able to recover successfully the true overall shape of the functional covariate-adjusted partial area under the curve in different complex scenarios Partially funded by Fondecyt Grants 11130541 (first author) and 11121186 (second author). Supported in part by the Spanish Ministry of Science and Innovation through project MTM2008-03010 SI |
| Databáze: | OpenAIRE |
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