126 citations · 163 across the 7 of their papers we have counts for
8 papers
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…
Doubly robust estimation of marginal cumulative incidence curves for competing risk analysis
Patrick van Hage, Saskia le Cessie, Marissa C. van Maaren +2
Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper we discuss different methods to est…
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…
When accurate prediction models yield harmful self-fulfilling prophecies
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe +2
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisio…
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…
Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data
Ruth H. Keogh, Nan van Geloven
Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characte…
Time-lag bias induced by unobserved heterogeneity: comparing treated patients to controls with a different start of follow-up
Rik van Eekelen, Patrick M. M. Bossuyt, Nan van Geloven
In comparative effectiveness research, treated and control patients might have a different start of follow-up as treatment is often started later in the disease trajectory. This ty…
Prediction meets causal inference: the role of treatment in clinical prediction models
Nan van Geloven, Sonja Swanson, Chava Ramspek +7
In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medi…