112091 20240705134135.0 doi 10.3390/math10081221 sideral 128017 ART-2022-128017 eng Aznar-Gimeno, Rocío (orcid)0000-0003-1415-146X A stepwise algorithm for linearly combining biomakers under Youden Index maximisation 2022 Access copy available to the general public Unrestricted Combining 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 info:eu-repo/grantAgreement/ES/DGA/E46-20R info:eu-repo/grantAgreement/ES/DGA/T17-20R info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.4 2022 MATHEMATICS 23 / 329 = 0.07 2022 Q1 T1 0.446 2022 Computer Science (miscellaneous) 2022 Q2 Mathematics (miscellaneous) 2022 Q2 Engineering (miscellaneous) 2022 Q2 3.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Esteban, Luis M. (orcid)0000-0002-3007-302X Hoyo-Alonso, Rafael del (orcid)0000-0003-2755-5500 Borque-Fernando, Ángel Universidad de Zaragoza (orcid)0000-0003-0178-4567 Sanz, Gerardo Universidad de Zaragoza (orcid)0000-0002-6474-2252 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 1013 817 Universidad de Zaragoza Dpto. Cirugía Área Urología 10, 8 (2022), 1221 [26 pp.] Mathematics (Basel) Mathematics 2227-7390 472275 http://zaguan.unizar.es/record/112091/files/texto_completo.pdf Versión publicada 2691940 http://zaguan.unizar.es/record/112091/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:112091 articulos driver 2024-07-05-12:45:34 ARTICLE