Ruth Keogh

sequential_trials. R code for implementation of the simulation study described in the paper: "Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models"

superlearner_survival_tutorial. The super learner for time-to-event outcomes: A tutorial. Ruth Keogh, Karla Diaz-Ordaz, Nan van Geloven, Jon Michael Gran, Kamaryn Tanner. https://arxiv.org/abs/2509.03315

causal_sim. Simulating longitudinal data from marginal structural models using the additive hazards model. https://arxiv.org/abs/2002.03678

ABCi_data. "A benchmark causal inference (ABCi) data set: simulated data on time-varying treatments, confounders and outcomes based on patients with type 2 diabetes." Ruth Keogh, Nan van Geloven, Daniala Weir.

MI-CC. Multiple imputation (MI) for case-cohort and nested case-control studies. https://onlinelibrary.wiley.com/doi/full/10.1111/biom.12910

MI-TVE. Multiple imputation in Cox regression when there are time-varying effects of covariates. https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7842

landmark_CF. R code for implementation of methods referred to in the manuscript entitled "Dynamic prediction of survival in cystic fibrosis: A landmarking analysis using patient registry data". https://journals.lww.com/epidem/Fulltext/2019/01000/Dynamic_Prediction_of_Survival_in_Cystic_Fibrosis_.5.aspx

meas_error_handbook. Ruth H Keogh & Jonathan W Bartlett. Measurement error as a missing data problem. In: Handbook of Measurement Error and Variable Selection. 2019. To appear. https://arxiv.org/abs/1910.06443

BARS. R