G-formula with multiple imputation for causal inference with incomplete data

Bartlett, Jonathan W., Olarte Parra, Camila, Granger, Emily, Keogh, Ruth H., van Zwet, Erik W. and Daniel, Rhian M. ORCID: https://orcid.org/0000-0001-5649-9320 2025. G-formula with multiple imputation for causal inference with incomplete data. Statistical Methods in Medical Research 34 (6) , pp. 1130-1143. 10.1177/09622802251316971

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Abstract

G-formula is a popular approach for estimating the effects of time-varying treatments or exposures from longitudinal data. G-formula is typically implemented using Monte-Carlo simulation, with non-parametric bootstrapping used for inference. In longitudinal data settings missing data are a common issue, which are often handled using multiple imputation, but it is unclear how G-formula and multiple imputation should be combined. We show how G-formula can be implemented using Bayesian multiple imputation methods for synthetic data, and that by doing so, we can impute missing data and simulate the counterfactuals of interest within a single coherent approach. We describe how this can be achieved using standard multiple imputation software and explore its performance using a simulation study and an application from cystic fibrosis.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Medicine
Publisher:	SAGE Publications
ISSN:	0962-2802
Date of First Compliant Deposit:	11 April 2025
Last Modified:	10 Sep 2025 14:03
URI:	https://orca.cardiff.ac.uk/id/eprint/177604

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