G-Estimation of Structural Nested Models: Recent Applications in Two Subfields of Epidemiology

Abstract Correct adjustment for time-varying confounding affected by prior exposure is often not straightforward. G-estimation of structural nested models is a method of data analysis that allows for estimation of the combined effects of exposures that vary over time in a longitudinal cohort study. The method has not been widely adopted, but its use has increased in recent years, particularly in two subfields of epidemiology. Pharmacoepidemiologists have explored its applications to randomized trials with non-adherence or treatment switching and to finding optimal dynamic treatment regimens. Occupational epidemiologists have used it to correct for healthy worker survivor bias. Pharmacoepidemiologists have used simulations to illustrate extensions and novel applications, while occupational epidemiologists have focused on practical applications to observational data, often with careful attention to the handling of exposures. In theory, g-estimation of an appropriate structural nested model should be considered in the context of any longitudinal cohort in which at least one time-varying confounder is affected by prior exposure.
Source: Current Epidemiology Reports - Category: Epidemiology Source Type: research