3 citations · 4 across the 4 of their papers we have counts for
4 papers
The super learner for time-to-event outcomes: A tutorial
Ruth H. Keogh, Karla Diaz-Ordaz, Nan van Geloven +2
Estimating risks or survival probabilities conditional on individual characteristics based on censored time-to-event data is a commonly faced task. This may be for the purpose of d…
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represent…
Investigating the causal effects of multiple treatments using longitudinal data: a simulation study
Emily Granger, Gwyneth Davies, Ruth H. Keogh
Many clinical questions involve estimating the effects of multiple treatments using observational data. When using longitudinal data, the interest is often in the effect of treatme…
G-formula for causal inference via multiple imputation
Jonathan W. Bartlett, Camila Olarte Parra, Emily Granger +3
G-formula is a popular approach for estimating treatment or exposure effects from longitudinal data that are subject to time-varying confounding. G-formula estimation is typically…