Stability of clinical prediction models developed using statistical or machine learning methods

Cites Work

Unnamed Item
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Stabilizing the Lasso against cross-validation variability
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Subsampling versus bootstrapping in resampling-based model selection for multivariable regression
Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
On stability issues in deriving multivariable regression models
Robust Locally Weighted Regression and Smoothing Scatterplots
Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy
Variable selection – A review and recommendations for the practicing statistician
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 calculations for external validation of a clinical prediction model with a time-to-event outcome
Minimum sample size for external validation of a clinical prediction model with a continuous outcome
Selection of variables for multivariable models: opportunities and limitations in quantifying model stability by resampling
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
Minimum sample size for external validation of a clinical prediction model with a binary outcome