Sample size and predictive performance of machine learning methods with survival data: a simulation study

DOI10.1002/sim.9931zbMATH Open1540.62156MaRDI QIDQ6560569

Gabriele Infante, Rosalba Miceli, Federico Ambrogi

Publication date: 23 June 2024

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

zbMATH Keywords

predictionsimulationsample sizemachine learningtime-to-event

Mathematics Subject Classification ID

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

Cites Work

• Random survival forests
• Boosting algorithms: regularization, prediction and model fitting
• Selection of artificial neural network models for survival analysis with genetic algorithms
• Statistical modeling: The two cultures. (With comments and a rejoinder).
• Modeling survival data: extending the Cox model
• Continuous and discrete-time survival prediction with neural networks
• Multivariable Model‐Building
• Medical Risk Prediction
• Efron‐Type Measures of Prediction Error for Survival Analysis
• The elements of statistical learning. Data mining, inference, and prediction
• Using simulation studies to evaluate statistical methods
• Minimum sample size for developing a multivariable prediction model. II: Binary and time-to-event outcomes
• Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome

Related Items (1)

The use of fast approximate random forests using subsampling (rfsrc.fast) does not support the conclusion on sample sizes needed for random survival forests

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