5 citations · 15 across the 17 of their papers we have counts for
19 papers
On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +2
Estimating the average causal effect (ACE) using observational data is a key focus in causal inference for which missing data present an important challenge. Multiple imputation (M…
Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
Tong Chen, Stijn Vansteelandt, David Burgner +2
Causal mediation analysis examines causal pathways linking exposures to disease. The estimation of interventional effects, which are mediation estimands that overcome certain ident…
A roadmap for systematic identification and analysis of multiple biases in causal inference
Rushani Wijesuriya, Rachael A. Hughes, John B. Carlin +3
Observational studies examining causal effects rely on unverifiable assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine…
Sensitivity analysis for multivariable missing data using multiple imputation: a tutorial
Cattram D Nguyen, Katherine J Lee, Ian R White +2
Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable m…
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…
On the uses and abuses of regression models: a call for reform of statistical practice and teaching
John B. Carlin, Margarita Moreno-Betancur
Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for w…
Recoverability and estimation of causal effects under typical multivariable missingness mechanisms
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +2
In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends on causal and missingness assumptions. The latter can be depicted…
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…
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…
Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators
Margarita Moreno-Betancur, Paul Moran, Denise Becker +2
Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the bene…
Joint longitudinal and time-to-event models for multilevel hierarchical data
Samuel L. Brilleman, Michael J. Crowther, Margarita Moreno-Betancur +6
Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to h…