CutoffTests

CutoffTests refers to a family of statistical and data analysis procedures aimed at identifying optimal threshold values for converting a continuous predictive score into a binary decision. The underlying goal is to separate observations into two classes with a chosen criterion such as maximizing correct classifications, maximizing the balance of sensitivity and specificity, or minimizing expected misclassification costs.

Common approaches include threshold selection on receiver operating characteristic (ROC) curves, where the cutoff is chosen to maximize Youden's index (sensitivity plus specificity minus one) or to meet a predefined target for either sensitivity or specificity. Grid search across possible cutoffs on a validation set is another straightforward method, sometimes combined with cross-validation to assess stability. In some contexts, cost-sensitive or prevalence-adjusted cutoffs are derived to reflect different misclassification penalties or base rates.

Workflow generally involves fitting a score or model on training data, evaluating performance across a range of candidate thresholds on a validation set, selecting the cutoff, and reporting performance on an independent test set. Calibration checks may accompany cutoff selection to ensure predicted probabilities align with observed frequencies and to avoid miscalibration.

CutoffTests are widely used in medical diagnostics, risk scoring, quality control, and any domain where a continuous score must yield a binary decision. Examples include choosing a biomarker threshold for disease status or a credit risk score cutoff for loan approvals.

Limitations include sensitivity to sample size and class imbalance, potential instability across datasets, and the risk of overfitting with data-driven cutoffs. Threshold choice should be reported alongside metrics and, when possible, validated in external data.

See also: ROC analysis, Youden's index, thresholding, calibration.