A structural mean model to allow for noncompliance in a randomized trial comparing 2 active treatments

Abstract

We propose a structural mean modeling approach to obtain compliance-adjusted estimates for treatment effects in a randomized-controlled trial comparing 2 active treatments. The model relates an individual's observed outcome to his or her counterfactual untreated outcome through the observed receipt of active treatments. Our proposed estimation procedure exploits baseline covariates that predict compliance levels on each arm. We give a closed-form estimator which allows for differential and unexplained selectivity (i.e. noncausal compliance-outcome association due to unobserved confounding) as well as a nonparametric error distribution. In a simple linear model for a 2-arm trial, we show that the distinct causal parameters are identified unless covariate-specific expected compliance levels are proportional on both treatment arms. In the latter case, only a linear contrast between the 2 treatment effects is estimable and may well be of key interest. We demonstrate the method in a clinical trial comparing 2 antidepressants.