What’s new in the quantification of causal effects from longitudinal cohort studies: a brief introduction to marginal structural models for intensivists

Excerpt

When randomized controlled trials are not possible, observational longitudinal data may be the only option to quantify the impact of a treatment on an outcome [1]. As an illustration, we assessed the impact of empirical systemic antifungal therapy (SAT) on mortality in critically ill patients [2]. A regression may be used to model the relationship between SAT and mortality, with adjustment on potential confounders. In many situations, the regression coefficient cannot be interpreted causally, particularly when SAT administration is a time-dependent variable [3‐5]. Indeed, besides a direct causal effect, there may be several paths linking the treatment to the outcome through various confounders, i.e., variables associated with treatment allocation and with the outcome which may confound the association of interest. In this situation, the association measure provided by standard regression may differ from what clinicians often seek, which is a quantification of the direct causal effect between an exposure and an outcome. This situation may be illustrated by considering severe sepsis as a single confounder. Indeed, severe sepsis may trigger SAT while it also impacts mortality. Hence, SAT and mortality share a common cause, i.e., an indirect path linking SAT to mortality (Fig. E1 in the Electronic Supplementary Material). The standard approach based on logistic regression, for instance, may lead to a biased estimation of the causal effect of SAT on mortality because some time-dependent variables (i.e., septic shock) which may be affected by previous treatment history can in turn affect both further treatments and the outcome [3]. To overcome the limitation of standard regression approaches, new specific statistical methods have been developed to handle this type of bias and are often referred to as causal inference methods. They were first introduced in intensive care unit (ICU) literature by Bekaert et al. [6]. …