000112091 001__ 112091
000112091 005__ 20240705134135.0
000112091 0247_ $$2doi$$a10.3390/math10081221
000112091 0248_ $$2sideral$$a128017
000112091 037__ $$aART-2022-128017
000112091 041__ $$aeng
000112091 100__ $$0(orcid)0000-0003-1415-146X$$aAznar-Gimeno, Rocío
000112091 245__ $$aA stepwise algorithm for linearly combining biomakers under Youden Index maximisation
000112091 260__ $$c2022
000112091 5060_ $$aAccess copy available to the general public$$fUnrestricted
000112091 5203_ $$aCombining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer database
000112091 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/DGA/T17-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000112091 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000112091 590__ $$a2.4$$b2022
000112091 592__ $$a0.446$$b2022
000112091 591__ $$aMATHEMATICS$$b23 / 329 = 0.07$$c2022$$dQ1$$eT1
000112091 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ2
000112091 593__ $$aMathematics (miscellaneous)$$c2022$$dQ2
000112091 593__ $$aEngineering (miscellaneous)$$c2022$$dQ2
000112091 594__ $$a3.5$$b2022
000112091 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000112091 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis M.
000112091 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000112091 700__ $$0(orcid)0000-0003-0178-4567$$aBorque-Fernando, Ángel$$uUniversidad de Zaragoza
000112091 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, Gerardo$$uUniversidad de Zaragoza
000112091 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000112091 7102_ $$11013$$2817$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Urología
000112091 773__ $$g10, 8 (2022), 1221 [26 pp.]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000112091 8564_ $$s472275$$uhttps://zaguan.unizar.es/record/112091/files/texto_completo.pdf$$yVersión publicada
000112091 8564_ $$s2691940$$uhttps://zaguan.unizar.es/record/112091/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000112091 909CO $$ooai:zaguan.unizar.es:112091$$particulos$$pdriver
000112091 951__ $$a2024-07-05-12:45:34
000112091 980__ $$aARTICLE