5 citations · 15 across the 17 of their papers we have counts for
5 papers · 1 filter
Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +3
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, s…
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
Susan Ellul, Stijn Vansteelandt, John B. Carlin +1
Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is chall…
The ideal trial: defining causal estimands that balance relevance and feasibility in target trial emulations and actual randomized trials
Margarita Moreno-Betancur, Rushani Wijesuriya, John B. Carlin
Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in bot…
Multiple imputation for longitudinal data: A tutorial
Rushani Wijesuriya, Margarita Moreno-Betancur, John B Carlin +3
Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably…
Handling multivariable missing data in causal mediation analysis estimating interventional effects
S. Ghazaleh Dashti, Katherine J. Lee, Julie A. Simpson +2
The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research, given its potential to address policy-relevant questions about hy…