Separable effects for adherence

Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal “per-protocol” estimands. However, when sustained use is challenging to satisfy in practice, the usefulness of these estimands can be limited. Here we propose a different class of estimands: separable effects for adherence. These estimands compare modified medications, holding fixed a component responsible for nonadherence. Under assumptions about treatment components’ mechanisms of effect, a separable effects estimand can quantify the effectiveness of medication initiation strategies on an outcome of interest under the adherence mechanism of one of the medications. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators.