204 citations · 383 across the 11 of their papers we have counts for
7 papers · 1 filter
Population-Level Decision Curve Analysis May Mislead the Evaluation of Prediction Model Usefulness under Subgroup Utility Heterogeneity
Junfeng Wang, Kim Zhipei Wang, Ben Van Calster +3
Background Decision curve analysis (DCA) evaluates prediction model usefulness using net benefit (NB). Population-level NB is often interpreted as a proxy for population-level expe…
The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Lasai Barreñada, Ewout W Steyerberg, Dirk Timmerman +3
Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view re…
missForestPredict -- Missing data imputation for prediction settings
Elena Albu, Shan Gao, Laure Wynants +1
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestP…
Understanding overfitting in random forest for probability estimation: a visualization and simulation study
Lasai Barreñada, Paula Dhiman, Dirk Timmerman +2
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although…
How to develop, externally validate, and update multinomial prediction models
Celina K Gehringer, Glen P Martin, Ben Van Calster +3
Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. Howe…
Minimum Sample Size for Developing a Multivariable Prediction Model using Multinomial Logistic Regression
Alexander Pate, Richard D Riley, Gary S Collins +4
Multinomial logistic regression models allow one to predict the risk of a categorical outcome with more than 2 categories. When developing such a model, researchers should ensure t…
Risk prediction models for discrete ordinal outcomes: calibration and the impact of the proportional odds assumption
Michael Edlinger, Maarten van Smeden, Hannes F Alber +2
Calibration is a vital aspect of the performance of risk prediction models, but research in the context of ordinal outcomes is scarce. This study compared calibration measures for…