000109040 001__ 109040
000109040 005__ 20240705134134.0
000109040 0247_ $$2doi$$a10.3390/math9192497
000109040 0248_ $$2sideral$$a125212
000109040 037__ $$aART-2021-125212
000109040 041__ $$aeng
000109040 100__ $$0(orcid)0000-0003-1415-146X$$aAznar-Gimeno, Rocío
000109040 245__ $$aIncorporating a New Summary Statistic into the Min–Max Approach: A Min–Max–Median, Min–Max–IQR Combination of Biomarkers for Maximising the Youden Index
000109040 260__ $$c2021
000109040 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109040 5203_ $$aLinearly combining multiple biomarkers is a common practice that can provide a better diagnostic performance. When the number of biomarkers is sufficiently high, a computational burden problem arises. Liu et al. proposed a distribution-free approach (min–max approach) that linearly combines the minimum and maximum values of the biomarkers, involving only a single coefficient search. However, the combination of minimum and maximum biomarkers alone may not be sufficient in terms of discrimination. In this paper, we propose a new approach that extends that of Liu et al. by incorporating a new summary statistic, specifically, the median or interquartile range (min–max–median and min–max–IQR approaches) in order to find the optimal combination that maximises the Youden index. Although this approach is more computationally intensive than the one proposed by Liu et al, it includes more information and the number of parameters to be estimated remains reasonable. We compare the performance of the proposed approaches (min–max–median and min–max–IQR) with the min–max approach and logistic regression. For this purpose, a wide range of different simulated data scenarios were explored. We also apply the approaches to two real datasets (Duchenne Muscular Dystrophy and Small for Gestational Age).
000109040 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-17R$$9info:eu-repo/grantAgreement/ES/DGA-FSE/IODIDE research group
000109040 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109040 590__ $$a2.592$$b2021
000109040 592__ $$a0.538$$b2021
000109040 594__ $$a2.9$$b2021
000109040 591__ $$aMATHEMATICS$$b21 / 333 = 0.063$$c2021$$dQ1$$eT1
000109040 593__ $$aEngineering (miscellaneous)$$c2021$$dQ2
000109040 593__ $$aComputer Science (miscellaneous)$$c2021$$dQ2
000109040 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109040 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis M.
000109040 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, Gerardo$$uUniversidad de Zaragoza
000109040 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000109040 700__ $$0(orcid)0000-0001-9585-0187$$aSavirón-Cornudella, Ricardo$$uUniversidad de Zaragoza
000109040 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000109040 7102_ $$11003$$2027$$aUniversidad de Zaragoza$$bDpto. Anatom.Histolog.Humanas$$cArea Anatom.Embriol.Humana
000109040 773__ $$g9, 19 (2021), 2497 [17 pp.]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000109040 8564_ $$s489030$$uhttps://zaguan.unizar.es/record/109040/files/texto_completo.pdf$$yVersión publicada
000109040 8564_ $$s2736105$$uhttps://zaguan.unizar.es/record/109040/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109040 909CO $$ooai:zaguan.unizar.es:109040$$particulos$$pdriver
000109040 951__ $$a2024-07-05-12:45:01
000109040 980__ $$aARTICLE