CutoffWerte

Cutoffwerte, often called cutoff values, are threshold numbers used to separate measurement results into distinct categories. They turn a continuous variable into a binary or multi‑class decision, such as positive versus negative, high versus low, or risk strata.

Cutoffwerte can be fixed, defined by guidelines or regulatory standards, or they can be derived from data. They play a central role in diagnostics, risk assessment, screening, quality control, and decision making in research and practice. The choice of cutoffwerte determines how many cases are classified in each category and influences downstream actions.

Several methods exist to determine cutoffwerte. Receiver operating characteristic (ROC) analysis is commonly used to balance sensitivity and specificity. The Youden index can identify the point that optimizes this balance. Cutoffs can also be percentile-based, such as tertiles or quartiles, or based on costs and benefits in a decision‑analytic framework. In practice, cutoffs may reflect population-specific distributions and clinical guidelines.

Applications of Cutoffwerte span medicine, laboratory testing, environmental monitoring, and machine learning. In diagnostics, they define a test as positive or negative. In quality control, they separate pass from fail results. In predictive modeling, they set thresholds on probability outputs to produce actionable categories.

Limitations and caveats exist. Cutoffwerte depend on the studied population and sample distribution, and may not generalize. Dichotomizing continuous data can discard information and reduce statistical power. They may require recalibration over time and across different contexts, and multiple thresholds can complicate interpretation.