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Related Experiment Video

Updated: May 13, 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

External validation of a Cox prognostic model: principles and methods.

Patrick Royston¹, Douglas G Altman

¹Hub for Trials Methodology Research, MRC Clinical Trials Unit and University College London, Aviation House 125, Kingsway Country, London WC2B 6NH, UK. pr@ctu.mrc.ac.uk

BMC Medical Research Methodology

|March 19, 2013

Summary

External validation of Cox models is crucial for clinical practice. This study presents methods for validating Cox models using varying levels of published information, aiding researchers in assessing model performance.

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Published on: October 23, 2020

Area of Science:

Biostatistics
Clinical Epidemiology
Prognostic Modeling

Background:

External validation is essential for prognostic models to ensure clinical utility.
External validation of Cox models is less explored than for logistic regression models.
Satisfactory discrimination and calibration are key metrics for successful model validation.

Purpose of the Study:

To describe statistical approaches for the external validation of published Cox models.
To address the challenges in validating Cox models due to unspecified baseline functions.
To provide a toolkit for researchers conducting external validation of Cox models.

Main Methods:

Methods are described based on the level of published information: prognostic index only, prognostic index with Kaplan-Meier curves, and inclusion of the baseline survival curve.
A method for approximating the baseline survival function is proposed for assessing calibration.
The approaches were applied to two primary breast cancer datasets, one as derivation and one as validation sample.

Main Results:

The study demonstrates the application of validation methods to primary breast cancer datasets.
Results for discrimination and calibration are presented.
Plots of survival probabilities are provided to aid in model evaluation.

Conclusions:

The proposed validation methods are applicable to diverse prognostic studies.
The methods offer a practical toolkit for the external validation of Cox models.
Successful validation ensures the reliable performance of prognostic models in new patient populations.