Author:
Zhang Jiaxin,Dashti S Ghazaleh,Carlin John B.,Lee Katherine J.,Moreno-Betancur Margarita
Abstract
Abstract
Background
Despite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects in epidemiological studies with a single-point exposure and outcome. Missing data are common in these studies, and complete-case analysis (CCA) and multiple imputation (MI) are two frequently used methods for handling them. In randomised controlled trials (RCTs), it has been shown that MI should be conducted separately by treatment group. In observational studies, causal inference is now understood as the task of emulating an RCT, which raises the question of whether MI should be conducted by exposure group in such studies.
Methods
We addressed this question by evaluating the performance of seven methods for handling missing data when estimating causal effects with outcome regression. We conducted an extensive simulation study based on an illustrative case study from the Victorian Adolescent Health Cohort Study, assessing a range of scenarios, including seven outcome generation models with exposure-confounder interactions of differing strength.
Results
The simulation results showed that MI by exposure group led to the least bias when the size of the smallest exposure group was relatively large, followed by MI approaches that included the exposure-confounder interactions.
Conclusions
The findings from our simulation study, which was designed based on a real case study, suggest that current practice for the conduct of MI in causal inference may need to shift to stratifying by exposure group where feasible, or otherwise including exposure-confounder interactions in the imputation model.
Funder
Melbourne Research Scholarship
Statistical Society of Australia top-up scholarship
Australian National Health and Medical Research Council
Australian Research Council Discovery Early Career Researcher Award
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Cited by
6 articles.
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