1 1 Département de Mathématiques, Université du Québec à Montréal, Québec, Canada.
2 2 Département de Médecine Sociale et préventive, Université Laval, Québec, Canada.
3 3 Unité Santé des Populations et Pratiques Optimales en Santé, Centre de recherche du CHU de Québec - Université Laval, Québec, Canada.
4 4 Department of Exercise Science, Concordia University, Québec, Canada.
5 5 Montreal Behavioural Medicine Centre, CIUSS-NIM, Hôpital du Sacré-Coeur de Montréal, Québec, Canada.
6 6 Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre, Québec, Canada.

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

Stat Methods Med Res. 2018 Aug;27(8):2428-2436

Authors: Talbot D, Rossi AM, Bacon SL, Atherton J, Lefebvre G

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 are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models 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 marginal structural models. 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 marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.

PMID: 27920366 [PubMed - in process]