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

Statistical Methods in Medical Research, vol. 34, pp. 1130–1143

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.

Authors 5

1. Jonathan William Bartlett corresponding

   London School of Hygiene & Tropical Medicine

   Affiliation as printed

   London School of Hygiene & Tropical Medicine

   Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK

2. Camila Olarte Parra

   London School of Hygiene & Tropical Medicine

   Affiliation as printed

   London School of Hygiene & Tropical Medicine

   Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK

3. Ruth H. Keogh

   London School of Hygiene & Tropical Medicine

   Affiliation as printed

   London School of Hygiene & Tropical Medicine

   Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK

4. Erik W. van Zwet Aachen Department of Biomedical Data Sciences

   Leiden University

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University, Leiden, the Netherlands

5. Rhian Daniel

   Cardiff University

   Affiliation as printed

   Cardiff University

   Division of Population Medicine, Cardiff University, Cardiff, UK