2 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
Framework for the Treatment And Reporting of Missing data in Observational Studies: The TARMOS framework
Katherine J Lee, Kate Tilling, Rosie P Cornish +5
Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, parti…
A review and evaluation of standard methods to handle missing data on time-varying confounders in marginal structural models
Clemence Leyrat, James R Carpenter, Sebastien Bailly +1
Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal non-randomised studies. A common issue when analysing data from observat…
Propensity scores using missingness pattern information: a practical guide
Helen A. Blake, Clemence Leyrat, Kathryn E. Mansfield +4
Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to addres…
Local average treatment effects estimation via substantive model compatible multiple imputation
Karla DiazOrdaz, James Carpenter
Non-adherence to assigned treatment is common in randomised controlled trials (RCTs). Recently, there has been an increased interest in estimating causal effects of treatment recei…
Information-Anchored Sensitivity Analysis: Theory and Application
Suzie Cro, James R Carpenter, Michael G Kenward
Analysis of longitudinal randomised controlled trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we…
Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models
Tra My Pham, James R Carpenter, Tim P Morris +2
Multiple imputation (MI) has become popular for analyses with missing data in medical research. The standard implementation of MI is based on the assumption of data being missing a…