Simulation on GUSTO-I data shows class imbalance corrections fail to boost discrimination and impair calibration plus stability in clinical prediction models.
Minimum sample size for developing a multivariable prediction model: P ART II - binary and time-to-event outcomes
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Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.
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Class Imbalance Corrections Failed to Enhance Discrimination, Model Calibration, and Prediction Stability: An Empirical Simulation Study Based on Clinical Dataset
Simulation on GUSTO-I data shows class imbalance corrections fail to boost discrimination and impair calibration plus stability in clinical prediction models.
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Influence of continuous predictor modelling methods on prediction stability in clinical prediction model development: an empirical comparison using real clinical data
Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.