Key result
Review discusses statistical approaches to define clinical variable cut-offs without presenting new endpoint data.
Why the study?
Arbitrary or poorly justified cut-off points for continuous variables in clinical research can cause substantial information loss, reduced statistical power, and misleading conclusions.
Design
Review
Arbitrary dichotomization of continuous variables should be avoided to prevent information loss and biased effect estimates; when cut-offs are necessary, they must be clinically justified and transparently reported.
Continuous variables are often dichotomized or categorized in clinical research to improve interpretability or to align with clinical thresholds. However, arbitrary or poorly justified cut-off points can cause substantial information loss, reduced statistical power, and potentially misleading conclusions. In this article, we describe commonly used approaches for determining cut-off points, including guideline-based thresholds, median, or quantile splits, and statistically derived methods, such as receiver operating characteristic (ROC) curve-based approaches (e.g., Youden Index and related criteria). We also discuss the clinical and methodological implications of these approaches using illustrative examples and offer practical recommendations to support the transparent and appropriate use of cut-offs in anesthesia and perioperative research.
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HONG et al. (2026) studied this question. The article discusses various statistical approaches to define cut-off points for clinical variables but does not present specific numerical results or primary endpoint data.
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