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Related Experiment Video

Updated: Mar 11, 2026

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

A closed testing procedure to select an appropriate method for updating prediction models.

Yvonne Vergouwe¹, Daan Nieboer¹, Rianne Oostenbrink²

¹Center for Medical Decision Sciences, Department of Public Health, Erasmus MC, Rotterdam, the Netherlands.

Statistics in Medicine

|November 29, 2016

Summary

A new closed testing procedure helps select appropriate model updating methods for logistic regression prediction models. This strategy balances updating evidence with overfitting risks, improving model performance in new populations.

Keywords:

closed testing procedure logistic regression model updating prediction model

Related Experiment Videos

Last Updated: Mar 11, 2026

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

Biostatistics
Clinical Epidemiology

Background:

Logistic regression prediction models often perform poorly in populations different from their development sample.
Model updating strategies exist but vary in their extensiveness and risk of overfitting.

Purpose of the Study:

To define a strategy for selecting appropriate prediction model update methods.
To balance the evidence for updating with the risk of overfitting in new patient samples.

Main Methods:

Considered recalibration in the large, recalibration, and model revision as update methods.
Proposed a closed testing procedure for progressively increasing update extensiveness.
Maintained approximate type I error rate through multiple testing.

Main Results:

The closed testing procedure selected recalibration in the large for a prostate cancer model, avoiding overfitting seen with full model revision.
The procedure demonstrated advantages in traumatic brain injury and pediatric fever examples.
The method effectively balanced updating needs and overfitting risks.

Conclusions:

The proposed closed testing procedure is a useful tool for selecting appropriate update methods for existing prediction models.
This approach can enhance the generalizability and reliability of prediction models across different populations.