5 citations · 15 across the 17 of their papers we have counts for
4 papers · 2 filters
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…
Confounding-adjustment methods for the causal difference in medians
Daisy A. Shepherd, Benjamin R. Baer, Margarita Moreno-Betancur
With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expec…
Causal inference in multi-cohort studies using the target trial framework to identify and minimize sources of bias
Marnie Downes, Meredith O'Connor, Craig A. Olsson +5
Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Uti…
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…