4 citations · 6 across the 7 of their papers we have counts for
8 papers
Discrimination performance in illness-death models with interval-censored disease data
Marta Spreafico, Anja J. Rueten-Budde, Hein Putter +1
In clinical studies, the illness-death model is often used to describe disease progression. A subject starts disease-free, may develop the disease and then die, or die directly. In…
An Overview and Recent Developments in the Analysis of Multistate Processes
Malka Gorfine, Richard J. Cook, Per Kragh Andersen +5
Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also…
Non-parametric estimation of transition intensities in interval censored Markov multi-state models without loops
Daniel Gomon, Hein Putter
Interval-censored multi state data is collected when the state of a subject is observed periodically. The analysis of such data using non-parametric multi-state models was not poss…
The causal interpretation of acceleration factors
Mari Brathovde, Hein Putter, Morten Valberg +1
In studies of time-to-event outcomes with unmeasured heterogeneity, the hazard ratio for treatment is known to have a complex causal interpretation. Accelerated failure time (AFT)…
A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records
Shan Gao, Elena Albu, Hein Putter +7
Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We a…
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…
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…