Estimation of local average treatment effects in randomized trials typically relies upon the exclusion restriction assumption in cases where we are unwilling to rule out the possibility of unmeasured confounding. Under this assumption, treatment effects are mediated through the post-randomization variable being conditioned upon, and directly attributable to neither the randomization itself nor its latent descendants. Recently, there has been interest in mobile health interventions to provide healthcare support. Mobile health interventions such as the Rapid Encouragement/Education and Communications for Health (REACH), designed to support self-management for adults with type 2 diabetes, often involve both one-way and interactive messages. In practice, it is highly likely that any benefit from the intervention is achieved both through receipt of the intervention content and through engagement with/response to it. Application of an instrumental variable analysis in order to understand the role of engagement with REACH (or a similar intervention) requires the traditional exclusion restriction assumption to be relaxed. We propose a conceptually intuitive sensitivity analysis procedure for the REACH randomized trial that places bounds on local average treatment effects. Simulation studies reveal this approach to have desirable finite-sample behavior and to recover local average treatment effects under correct specification of sensitivity parameters.
Keywords: Causal; exclusion restriction; instrumental variables; sensitivity analysis.