1 citations · 1 across the 4 of their papers we have counts for
4 papers
Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models
Ruth H. Keogh, Jon Michael Gran, Shaun R. Seaman +2
Longitudinal observational patient data can be used to investigate the causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for…
A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models
N. Maltzahn, R. Hoff, O. O. Aalen +3
Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumpti…
Simulating longitudinal data from marginal structural models using the additive hazard model
Ruth H. Keogh, Shaun R. Seaman, Jon Michael Gran +1
Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time-dependent confounding. Marginal s…
Dynamic models for estimating the effect of HAART on CD4 in observational studies: application to the Aquitaine Cohort study and the Swiss HIV Cohort Study
M. Prague, D. Commenges, J. M. Gran +4
Highly active antiretroviral therapy (HAART) has proved efficient in increasing CD4 counts in many randomized clinical trials. Because randomized trials have some limitations (e.g.…