4 citations · 8 across the 5 of their papers we have counts for
5 papers
Evaluating the impact of longitudinal treatment strategies in the presence of informative monitoring and time-dependent confounding
Leah Pirondini, Karla Diaz-Ordaz, Edward Palmer +1
Routinely collected data from electronic health records (EHR) provide opportunities to study effects of longitudinal treatment strategies in real-world clinical settings. A challen…
Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Est…
Multiple imputation of missing covariates when using the Fine-Gray model
Edouard F. Bonneville, Jan Beyersmann, Ruth H. Keogh +5
The Fine-Gray model for the subdistribution hazard is commonly used for estimating associations between covariates and competing risks outcomes. When there are missing values in th…
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…