Causal Inference
Separable effects for adherence Kerollos Wanis* Kerollos Wanis Mats Stensrud Aaron Sarvet
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 the usual causal `per-protocol’ estimand. However, when sustained use is challenging to satisfy in practice, the usefulness of this estimand 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 non-adherence. Under assumptions about treatment components’ mechanisms of effect, the separable effects estimand can eliminate differences in adherence. 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. We illustrate an application of the separable effects for adherence in simulated data based on a randomized trial in which investigators compare initiation of a thiazide diuretic or an angiotensin-converting enzyme inhibitor. The application considers a series of data generating mechanisms in order to give intuition for when separable effects assumptions will hold so that adherence is balanced.
