A stepwise algorithm for linearly combining biomakers under Youden Index maximisation

Aznar-Gimeno, Rocío ; Esteban, Luis M. ; Hoyo-Alonso, Rafael del ; Borque-Fernando, Ángel (Universidad de Zaragoza) ; Sanz, Gerardo (Universidad de Zaragoza)

Resumen: 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

Idioma: Inglés

DOI: 10.3390/math10081221

Año: 2022

Publicado en: Mathematics 10, 8 (2022), 1221 [26 pp.]
ISSN: 2227-7390

Factor impacto JCR: 2.4 (2022)

Categ. JCR: MATHEMATICS rank: 23 / 329 = 0.07 (2022) - Q1 - T1

Factor impacto CITESCORE: 3.5 - Engineering (Q2) - Mathematics (Q1) - Computer Science (Q2)

Factor impacto SCIMAGO: 0.446 - Computer Science (miscellaneous) (Q2) - Mathematics (miscellaneous) (Q2) - Engineering (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T17-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Urología (Dpto. Cirugía)