  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.
