A graphical perspective of marginal structural models : an application for the estimation of the effect of physical activity on blood pressure

Authors: Talbot, Denis; Rossi, Amanda M.; Bacon, Simon L.; Atherton, Juli; Lefebvre, Geneviève
Abstract: Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models (MSMs) are a relatively new class of causal models which effectively deal with the estimation of the effects of time-varying exposures. MSMs have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of MSMs. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time-points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using MSMs for repeated measures and guided the implementation of the models with the causal graph. Employing the causal graph framework, we also show the validity of fitting conditional MSMs for repeated measures in the context implied by our data.
Document Type: Article de recherche
Issue Date: 29 December 2016
Open Access Date: 9 April 2021
Document version: AM
Permalink: http://hdl.handle.net/20.500.11794/68718
This document was published in: Statistical methods in medical research, Vol. 27(8), 2428-2436 (2018)
https://doi.org/10.1177/0962280216680834
Edward Arnold
Alternative version: 10.1177/0962280216680834
27920366
Collection:Articles publiés dans des revues avec comité de lecture

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