Optimal Cut-Point Calculation of a Predictor Variable for Binary Classification: A Practical How-To Guide with Examples for Clinicians

Calculating the optimal cut-point of a predictor variable for a binary classification task is a crucial step in clinical practice, directly influencing diagnostic accuracy and patient management. This tutorial offers a practical, step-by-step guide for clinicians and researchers for optimal cut-point calculation and reporting, with a focus on transparency and reproducibility. We review key statistical concepts, including performance metrics and receiver operating characteristic (ROC) curve analysis, before detailing a recommended workflow encompassing exploratory data analysis, polarity identification, cut-point calculation, stability assessment, and post hoc verification. The approach is illustrated with real-world case studies using open clinical datasets, highlighting both the methodology and common challenges.