Generalizing and transporting causal inferences from randomized trials in the presence of trial engagement effects

TL;DR

This paper develops methods to generalize and transport causal inferences from randomized trials to broader populations, accounting for trial engagement effects that influence outcomes independently of treatment.

Contribution

It introduces novel causal estimands and identification results that incorporate trial engagement effects under no interaction assumptions, expanding the applicability of generalizability and transportability analyses.

Findings

Methods work even with trial engagement effects present.

Identifying functionals are the same as in previous models under no interaction.

Provides a new interpretation for existing estimators in the presence of engagement effects.

Abstract

Trial engagement effects are effects of trial participation on the outcome that are not mediated by treatment assignment. Most work on extending (generalizing or transporting) causal inferences from a randomized trial to a target population has, explicitly or implicitly, assumed that trial engagement effects are absent, allowing evidence about the effects of the treatments examined in trials to be applied to non-experimental settings. Here, we define novel causal estimands and present identification results for generalizability and transportability analyses in the presence of trial engagement effects. Our approach allows for trial engagement effects under assumptions of no causal interaction between trial participation and treatment assignment on the absolute or relative scales. We show that under these assumptions, even in the presence of trial engagement effects, the trial data can be combined with covariate data from the target population to identify average treatment effects in the context of usual care as implemented in the target population (i.e., outside the experimental setting). The identifying observed data functionals under these no-interaction assumptions are the same as those obtained under the stronger identifiability conditions that have been invoked in prior work. Therefore, our results suggest a new interpretation for previously proposed generalizability and transportability estimators; this interpretation may be useful in analyses under causal structures where background knowledge suggests that trial engagement effects are present but interactions between trial participation and treatment are negligible.