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Home
Bayesian Sample Size Calculations For External Validation Studies Of Risk Prediction Models.

Home
Bayesian Sample Size Calculations For External Validation Studies Of Risk Prediction Models.

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Bayesian Sample Size Calculations for External Validation Studies of Risk Prediction Models.

Mohsen Sadatsafavi¹, Paul Gustafson², Solmaz Setayeshgar³

¹Faculty of Pharmaceutical Sciences and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.

Statistics in Medicine

|February 12, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a Bayesian framework for sample size calculations in risk prediction model validation. It offers flexible rules considering uncertainty, improving precision and clinical utility assessments for better model evaluation.

Keywords:

Bayesian statistics decision theory risk prediction sample size uncertainty

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Area of Science:

Biostatistics
Epidemiology
Health Informatics

Background:

Current sample size calculations for external validation of risk prediction models rely on fixed performance metrics and precision targets.
This approach is limited by uncertainty in true model performance due to finite previous study samples.
Conventional methods are less suitable for assessing clinical utility using measures like net benefit (NB).

Purpose of the Study:

To propose a general Bayesian framework for multi-criteria sample size considerations for prediction models with binary outcomes.
To develop sample size rules for statistical performance metrics (discrimination, calibration) and clinical utility (net benefit).
To address limitations of conventional methods by incorporating uncertainty and providing flexible sample size determination.

Main Methods:

Developed a Bayesian framework for sample size calculations.
Proposed sample size rules targeting expected precision or assurance probability for performance metrics.
Introduced rules for net benefit based on Optimality Assurance and Value of Information (VoI) analysis.
Applied the framework to validate a COVID-19 patient deterioration risk model.

Main Results:

The Bayesian approach quantifies uncertainty in model performance, enabling flexible sample size rules.
Value of Information (VoI) analysis for net benefit suggested lower sample sizes compared to precision-based calibration metrics.
Demonstrated application in a case study for a COVID-19 risk prediction model.

Conclusions:

A Bayesian framework offers a more comprehensive approach to sample size calculation for external model validation.
This method allows for flexible sample size determination based on expected precision, assurance probabilities, and VoI.
The proposed approach, particularly VoI for net benefit, can lead to more efficient sample size requirements.