2 citations · 3 across the 7 of their papers we have counts for
7 papers · 1 filter
Evaluation of multiple imputation to address intended and unintended missing data in case-cohort studies with a binary endpoint
Melissa Middleton, Cattram Nguyen, John B. Carlin +2
Case-cohort studies are conducted within cohort studies, wherein collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by…
Statistical analyses of ordinal outcomes in randomised controlled trials: protocol for a scoping review
Chris J. Selman, Katherine J. Lee, Robert K. Mahar
Randomised controlled trials aim to assess the impact of one (or more) health interventions relative to other standard interventions. RCTs sometimes use an ordinal outcome, which i…
A comparison of strategies for selecting auxiliary variables for multiple imputation
Rheanna M. Mainzer, Cattram D. Nguyen, John B. Carlin +3
Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of…
Making SMART decisions in prophylaxis and treatment studies
Robert K. Mahar, Katherine J. Lee, Bibhas Chakraborty +2
The optimal prophylaxis, and treatment if the prophylaxis fails, for a disease may be best evaluated using a sequential multiple assignment randomised trial (SMART). A SMART is a m…
Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation
S. Ghazaleh Dashti, Katherine J. Lee, Julie A. Simpson +3
Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive app…
Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data
Rushani Wijesuriya, Margarita Moreno-Betancur, John B. Carlin +2
Three-level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such st…
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…