469 citations · 500 across the 8 of their papers we have counts for
8 papers
Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
Amardeep Legha, Joie Ensor, Rebecca Whittle +7
When prospectively developing a new clinical prediction model (CPM), fixed sample size calculations are typically conducted before data collection based on sensible assumptions. Bu…
Clustered Flexible Calibration Plots For Binary Outcomes Using Random Effects Modeling
Lasai Barreñada, Bavo D. C. Campo, Laure Wynants +1
Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment o…
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…
A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records
Shan Gao, Elena Albu, Hein Putter +7
Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We a…
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…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
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…
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.…