What question did this study set out to answer?

This research aims to develop a method for selecting cutoff values for continuous prognostic variables that show a U-shaped association with risk and account for competing risks.

August 2, 2026Communications for Statistical Applications and MethodsOpen Access

Optimal cutoff selection for U-shaped prognostic variable in survival data with competing risk

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This research aims to develop a method for selecting cutoff values for continuous prognostic variables that show a U-shaped association with risk and account for competing risks.
Developed the Competing Risk C-index based Cutoff (CRCC) method to identify cutoff values based on log-hazard heights.
Used smoothing splines in the Fine-Gray model to confirm U-shaped relationships and generate candidate cutoff pairs.
Compared CRCC with Gönen-Heller's CPE, Min-P, and percentile methods through Monte Carlo simulations.
CRCC using the Fine-Gray model achieved the lowest Bayesian information criterion (BIC) and smallest standard errors in simulations.
The competing risk model showed consistent performance for cutoff selection and risk stratification.
Other methods displayed greater variability and instability in selecting cutoff values.

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Implication

Randomized trial evaluates cutoff selection for U-shaped prognostic variables in survival data, suggesting a reliable method for risk stratification.

Cite This Study

Woo et al. (2026) studied this question.

synapsesocial.com/papers/6a6eeb4d1b0468a7eeab4698 https://doi.org/10.29220/csam.2026.33.4.541

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Implication

Randomized trial evaluates cutoff selection for U-shaped prognostic variables in survival data, suggesting a reliable method for risk stratification.

Optimal cutoff selection for U-shaped prognostic variable in survival data with competing risk

Key Points

Discussion

Implication

Cite This Study

Optimal cutoff selection for U-shaped prognostic variable in survival data with competing risk

Key Points

Discussion

Implication

Cite This Study

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