204 citations · 383 across the 11 of their papers we have counts for
5 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…
Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective
Koen M. F. Gorgels, Lasai Barreñada, Maarten van Smeden +3
Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decisions may depend on specific ris…
Collapsibility of Performance Metrics in Clinical Predictive AI
João Matos, Ben Van Calster, Richard D. Riley +2
Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups. Fairness evaluations commonly rely on perf…
Efficient Variance Estimation for the Polytomous Discrimination Index
Qunqiang Feng, Taozheng Jia, Pan Liu +2
Evaluating diagnostic accuracy for multi-category outcomes remains a significant challenge, primarily due to computational limitations in existing performance metrics. The Polytomo…
Value-of-Information Analysis for External Validation of Risk Prediction Models in Multicenter Studies and Systematic Reviews
Laure Wynants, Kim Zhipei Wang, Sabine Grimm +5
External validation studies have finite sample sizes, creating uncertainty about whether a prediction model's Net Benefit (NB) exceeds default strategies' NB. The expected value of…