Inference of ROC Curves via Yeo-Johnson Transformation for Single and Correlated Biomarkers

Thomas M. Menino Convention & Exhibition Center 

Evaluation of diagnostic tests and biomarkers using parametric ROC methods often requires that data be transformable to an approximately normal distribution. The Box-Cox transformation is commonly used but is restricted to non-negative values and therefore cannot be applied to data containing both negative and positive measurements. The Yeo-Johnson transformation is a flexible alternative that can accommodate both negative and positive observations; however, its use for ROC-based inference has not been formally developed. In this study, we explore an ROC inference framework based on the Yeo–Johnson transformation that accounts for estimation variability of the transformation parameter. These include key ROC functionals such as the AUC, the Youden index, and the sensitivity at a fixed specificity (or vice versa). We further extend this framework to paired designs, allowing comparison of correlated ROC curves when two biomarkers are measured on the same individuals. We evaluate the performance of the explored methods through simulation studies and further demonstrate them on real data involving pancreatic cancer biomarker data. Finally, we discuss an accompanying R package, rocYeoJ.

Biomarker Evaluation

Yeo–Johnson transformation

ROC curve inference

Sensitivity

Specificity

Correlated Biomarkers 

Section on Statistics in Genomics and Genetics