204 citations · 220 across the 8 of their papers we have counts for
9 papers
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…
Instability in Patient Clustering: A Multiverse Analysis of Unsupervised Clustering in the CENTER-TBI cohort
Sean R E A Bagcik, Aneeta Merlin Chacko, Ewout W Steyerberg +4
Understanding patient heterogeneity is key to improving prognostic modeling in traumatic brain injury (TBI). Unsupervised clustering is widely used to explore patterns in patient c…
Extended sample size calculations for evaluation of prediction models using a threshold for classification
Rebecca Whittle, Joie Ensor, Lucinda Archer +10
When evaluating the performance of a model for individualised risk prediction, the sample size needs to be large enough to precisely estimate the performance measures of interest.…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
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…
Imputation and Missing Indicators for handling missing data in the development and implementation of clinical prediction models: a simulation study
Rose Sisk, Matthew Sperrin, Niels Peek +2
Background: Existing guidelines for handling missing data are generally not consistent with the goals of prediction modelling, where missing data can occur at any stage of the mode…
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…
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold
Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a n…