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Updated: Jul 3, 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

The performance of risk prediction models.

Thomas A Gerds¹, Tianxi Cai, Martin Schumacher

¹Department of Biostatistics, Øster Farimagsgade 5 opg. B, Postboks 2099, 1014 København. tag@biostat.ku.dk

Biometrical Journal. Biometrische Zeitschrift

|July 30, 2008

Summary

This review details modern methods for assessing risk prediction models, crucial for medical decision-making. It emphasizes proper benchmarks and resampling for accurate performance interpretation in clinical studies.

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Last Updated: Jul 3, 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

Area of Science:

Biostatistics
Clinical Epidemiology
Health Informatics

Background:

Risk prediction models are vital for medical decision-making and patient information.
Numerous statistical approaches exist for deriving these models.
Assessing and comparing the predictive performance of these models is essential.

Purpose of the Study:

To provide a systematic review of contemporary methods for evaluating risk prediction models.
To highlight the importance of appropriate benchmarks and resampling techniques.
To illustrate these assessment methods using clinical data.

Main Methods:

Review of methodologies from ROC (Receiver Operating Characteristic) analysis and probability forecasting theory.
Application of performance measures to single markers, multivariable regression, and model selection algorithms.
Systematic literature review focusing on modern assessment techniques.

Main Results:

Identified key performance measures derived from ROC methodology and probability forecasting.
Demonstrated the application of these measures to various model types.
Illustrated the impact of benchmarks and resampling on performance interpretation.

Conclusions:

Modern assessment of risk prediction models requires rigorous application of established statistical principles.
Proper benchmarking and resampling are critical for reliable performance evaluation.
The presented methods offer a framework for assessing predictive models in clinical research, exemplified by head and neck cancer data.