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Simulating data from marginal structural models for a survival time outcome
Shaun R Seaman, Ruth H Keogh
Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be p…
Dynamic Updating of Clinical Survival Prediction Models in a Rapidly Changing Environment
Kamaryn Tanner, Ruth H. Keogh, Carol A. C. Coupland +2
Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction model…
Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data
Ruth H. Keogh, Nan van Geloven
Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characte…
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…
mecor: An R package for measurement error correction in linear regression models with a continuous outcome
Linda Nab, Maarten van Smeden, Ruth H. Keogh +1
Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated co…
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…