Estimating the average treatment effect in randomized clinical trials with all-or-none compliance

Noncompliance is a common intercurrent event in randomized clinical trials that raises important questions about analytical objectives and approaches. Motivated by the Multiple Risk Factor Intervention Trial (MRFIT), we consider how to estimate the average treatment effect (ATE) in randomized trials with all-or-none compliance. Confounding is a major challenge in estimating the ATE, and conventional methods for confounding adjustment typically require the assumption of no unmeasured confounders, which may be difficult to justify. Using randomized treatment assignment as an instrumental variable, the ATE can be identified in the presence of unmeasured confounders under suitable assumptions, including an assumption that limits the effect-modifying activities of unmeasured confounders. We describe and compare several estimation methods based on different modeling assumptions. Some of these methods are able to incorporate information from auxiliary covariates for improved efficiency without introducing bias. The different methods are compared in a simulation study and applied to the MRFIT.