Minimum sample size for developing a multivariable prediction model. I: Continuous outcomes

DOI10.1002/sim.7993zbMATH Open1545.62517MaRDI QIDQ6625938

Unnamed Author, Joie Ensor, Gary S. Collins, Danielle L. Burke, Frank E. jun. Harrell, Kym I. E. Snell, Richard D. Riley Riley

Publication date: 28 October 2024

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

zbMATH Keywords

linear regressionminimum sample sizeR-squaredmultivariable prediction modelcontinuous outcome

Mathematics Subject Classification ID

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

Cites Work

• 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
• Minimum sample size for developing a multivariable prediction model. II: Binary and time-to-event outcomes
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Related Items (6)

Minimum sample size for external validation of a clinical prediction model with a continuous outcome ⋮ Minimum sample size for external validation of a clinical prediction model with a binary outcome ⋮ Stability of clinical prediction models developed using statistical or machine learning methods ⋮ Regularized parametric survival modeling to improve risk prediction models ⋮ Minimum sample size for developing a multivariable prediction model. II: Binary and time-to-event outcomes ⋮ A note on estimating the Cox-Snell \(R^2\) from a reported \(C\) statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome

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