activity
20182024
most citedPrediction meets causal inference: the role of treatment in clinical prediction models

126 citations · 136 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2024

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…

stat.ME2024★ 1 cited

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…

stat.ME2023★ 4 cited

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…

stat.ME2023★ 2 cited

Replicability of Simulation Studies for the Investigation of Statistical Methods: The RepliSims Project

K. Luijken, A. Lohmann, U. Alter +13

Results of simulation studies evaluating the performance of statistical methods are often considered actionable and thus can have a major impact on the way empirical research is im…

stat.ME2020★ 126 cited

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…

stat.ME2018★ 3 cited

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.…