160 citations · 289 across the 12 of their papers we have counts for
12 papers
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…
Investigating Impacts of Health Policies Using Staggered Difference-in-Differences: The Effects of Adoption of an Online Consultation System on Prescribing Patterns of Antibiotics
Kate B. Ellis, Ruth H. Keogh, Geraldine M. Clarke +1
We use a recently proposed staggered difference-in-differences approach to investigate effects of adoption of an online consultation system in English general practice on antibioti…
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…
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…
Multiple imputation in Cox regression when there are time-varying effects of exposures
Ruth H. Keogh, Tim P. Morris
In Cox regression it is sometimes of interest to study time-varying effects (TVE) of exposures and to test the proportional hazards assumption. TVEs can be investigated with log ha…
Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration
Jonathan W. Bartlett, Ruth H. Keogh
Bayesian approaches for handling covariate measurement error are well established, and yet arguably are still relatively little used by researchers. For some this is likely due to…