14 citations · 26 across the 5 of their papers we have counts for
5 papers
Performance evaluation of predictive AI models to support medical decisions: Overview and guidance
Ben Van Calster, Gary S. Collins, Andrew J. Vickers +11
A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is…
The harms of class imbalance corrections for machine learning based prediction models: a simulation study
Alex Carriero, Kim Luijken, Anne de Hond +3
Risk prediction models are increasingly used in healthcare to aid in clinical decision making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk…
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
Ruben van den Goorbergh, Maarten van Smeden, Dirk Timmerman +1
Methods to correct class imbalance, i.e. imbalance between the frequency of outcome events and non-events, are receiving increasing interest for developing prediction models. We ex…
Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models
Linda Nab, Rolf H. H. Groenwold, Maarten van Smeden +1
In observational research treatment effects, the average treatment effect (ATE) estimator may be biased if a confounding variable is misclassified. We discuss the impact of classif…
Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective
Kim Luijken, Rolf H. H. Groenwold, Ben van Calster +2
It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived.…