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Validation in prediction research: the waste by data splitting.

Ewout W Steyerberg¹

¹Professor of Clinical Biostatistics and Medical Decision Making, Chair, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands; Professor of Medical Decision Making, Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.

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Summary

Random data splitting for validating medical prediction models is inefficient. For large samples, focus on performance heterogeneity; for small samples, use cross-validation or bootstrapping instead.

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Area of Science:

Medical Statistics
Clinical Prediction Modeling

Background:

Accurate prediction of medical outcomes is crucial for clinical diagnosis and prognosis.
Major medical journals increasingly require external validation of prediction models.
The efficiency of standard data splitting methods for validation is questioned.

Purpose of the Study:

To evaluate the necessity and efficiency of random data splitting for validating prediction models.
To propose alternative validation strategies for different sample sizes.

Main Methods:

Analysis of data splitting techniques in the context of prediction model validation.
Comparison of random data splitting with cross-validation and bootstrapping.

Main Results:

Random data splitting may be resource-intensive and inefficient, especially in large datasets.
Assessing heterogeneity in model performance across different settings is more relevant for large samples.
Cross-validation and bootstrapping are more efficient for small sample validation.

Conclusions:

Random data splitting should be discontinued for validating prediction models.
Validation strategies should be tailored to sample size, prioritizing efficiency and external validity.