Marginal structural models (MSMs) are informative, account for poor treatment adherence or switching, and allow inclusion of more patients than simply censoring patients in an ‘as protocol’ (AP) analysis. MSMs provide insights into time-varying factors occurring after initiation of disease controller therapy that may affect treatment choices or outcomes, and are not apparent in intent-to-treat (ITT) or AP analyses.

Retrospective comparative effectiveness studies using ITT or AP analysis methods often fail to include treatment adherence or switching in their analyses, leading to biased effect estimates. Other time-varying factors such as acute exacerbations of chronic disease can affect treatment decisions and outcomes, and thus also introduce biases. In this analysis of retrospective data from two regional health systems, we demonstrate that failure to account for treatment adherence can make the outcomes of patients who use controller therapies concurrently appear to be significantly worse than those of patients who use these treatments independently.

Based on this effectiveness study, we find that MSMs may be a useful and informative complementary analysis to include in studies of treatment effectiveness in chronic disease where time-varying confounding is present, and switching between treatments is more common.