Minimum sample size for external validation of a clinical prediction model with a binary outcome

DOI10.1002/sim.9025zbMATH Open1546.62623MaRDI QIDQ6628454

Joie Ensor, Lucinda Archer, Maarten van Smeden, Thomas P. A. Debray, Gary S. Collins, Richard D. Riley Riley, Kym I. E. Snell

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

calibrationdiscriminationbinary outcomesnet benefitminimum sample sizemultivariable prediction modelexternal validation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis
• Clinical prediction models. A practical approach to development, validation, and updating.
• Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy
• Statistical inference for net benefit measures in biomarker validation studies
• The integrated calibration index (ICI) and related metrics for quantifying the calibration of logistic regression models
• Minimum sample size for developing a multivariable prediction model. I: Continuous outcomes
• Minimum sample size for developing a multivariable prediction model. II: Binary and time-to-event outcomes
• Minimum sample size for external validation of a clinical prediction model with a continuous outcome

Related Items (2)

Stability of clinical prediction models developed using statistical or machine learning methods ⋮ Calibration plots for multistate risk prediction models

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