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Updated: Jan 5, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Three myths about risk thresholds for prediction models.

Laure Wynants^1,2, Maarten van Smeden^3,4, David J McLernon⁵

¹KU Leuven Department of Development and Regeneration, Leuven, Belgium. laure.wynants@maastrichtuniversity.nl.

|October 26, 2019

Summary

Defining appropriate risk thresholds for clinical prediction models is crucial. Thresholds should reflect decision consequences and clinical context, not be universally fixed, to ensure proper patient risk stratification and intervention allocation.

Keywords:

Clinical risk prediction model Data science Decision support techniques Diagnosis Prognosis Risk Threshold

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Area of Science:

Clinical Epidemiology
Biostatistics
Decision Science

Background:

Clinical prediction models estimate future disease risk based on patient characteristics.
A key challenge is defining appropriate risk thresholds for intervention recommendations.
Current ad hoc threshold definitions may not align with clinical realities of false positive/negative costs.

Purpose of the Study:

To address common myths leading to inappropriate patient risk stratification.
To guide the selection of clinically sensible risk thresholds for prediction models.
To improve the clinical application of risk prediction models.

Main Methods:

Discussion of three common myths regarding risk thresholds.
Analysis of contexts where continuous risk estimates are more useful than stratification.
Argument for context-dependent threshold selection based on decision consequences.

Main Results:

Continuous risk estimates are often more valuable than risk stratification in counseling and shared decision-making.
Risk threshold selection must consider the consequences of subsequent clinical decisions.
No single universal threshold exists; optimal thresholds are context-dependent.

Conclusions:

Adopting context-dependent thresholds avoids inappropriate intervention allocation.
Well-calibrated prediction models improve clinical outcomes when used with appropriate thresholds.
Presenting results for multiple risk thresholds is recommended during model development and validation.