1 citations · 1 across the 5 of their papers we have counts for
6 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…
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…
Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models
Linda Nab, Rolf H. H. Groenwold, Maarten van Smeden +1
In observational research treatment effects, the average treatment effect (ATE) estimator may be biased if a confounding variable is misclassified. We discuss the impact of classif…
Measurement error as a missing data problem
Ruth H. Keogh, Jonathan W. Bartlett
This article focuses on measurement error in covariates in regression analyses in which the aim is to estimate the association between one or more covariates and an outcome, adjust…
Epidemiologic analyses with error-prone exposures: Review of current practice and recommendations
Pamela A. Shaw, Veronika Deffner, Ruth H. Keogh +5
Background: Variables in epidemiological observational studies are commonly subject to measurement error and misclassification, but the impact of such errors is frequently not appr…