4 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…
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…
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…