Bivariate Conditional ROC Curves via Distributional Regression
Ó. Lado-Baleato, S. Pérez-Fernández
The diagnosis of many diseases relies on the joint evaluation of multiple continuous biomarkers that are often correlated. Traditional ROC analysis assesses each marker separately, ignoring this dependence and the influence of clinical covariates such as age on their joint distribution. This limitation may lead to suboptimal patient classification and misdiagnosis in specific subgroups. We propose a method to estimate bivariate conditional ROC curves using multivariate distributional regression. The model captures covariate effects on marginal distributions and dependence structures, allowing nonlinear relationships. Covariate-specific ROC curves are derived from conditional likelihood ratio. It yields a more accurate assessment of diagnostic performance by accounting for markers correlation and covariate effects. The method is evaluated via simulations under multiple scenarios and illustrated in a diabetes study where age influences two glycemic biomarkers across two patient groups.
Keywords: Bivariate ROC curves, bivariate distributional regression, multiple biomarkers.
Scheduled
Biostatistics II
September 4, 2026 3:30 PM
Aula 24
Other papers in the same session
I. Galván Femenía, J. Saperas Riera, J. Palarea Albaladejo
P. Jurado Bascón, J. A. Villatoro García, P. Carmona Sáez
B. Monteiro, A. H. Tavares, T. Gregório
B. Piñeiro Lamas, A. López-Cheda, R. Cao