Abstract
To translate a continuous or ordinal diagnostic variable into a clinical decision, it is necessary to determine a cutoff point that dichotomizes the patients into distinct groups for risk stratification, disease diagnosis, and management of care. There are numerous existing criteria only for establishing optimal cut-points of unmatched or unpaired data. In the paired case-control design, data matching reduces the bias due to confounding factors or covariates. TrialNET clinical data includes biomarkers from islet autoantibody-positive and autoantibody-negative, family-matched siblings. Conditional ROC curves have been developed to appropriately accommodate correlated biomarkers arising from this matched case-control design. However, the approach for optimizing cut-points in matched settings has not been investigated. In this work, correlated biomarker data are transformed into an uncorrelated structure, then optimal cut-points are determined by traditional non-parametrical and parametrical approaches. The optimized cut-point separates the two groups with the least misclassification. A small p-value from the hypothesis test of independence assumption indicates a potential predictive biomarker. New hypergeometric distributions and exact tests overcome the conservative nature of Fisher's exact test.