Sense and sensitivity when correcting for observed exposures in randomized clinical trials

Standard intent-to-treat analyses of randomized clinical trials can yield biased estimates of treatment efficacy and toxicity when not all patients comply with their assigned treatment. Flexible methods have been proposed which correct for this by modelling expected contrasts between an individual's observed outcome and his/her potential outcome in the absence of exposure. Because such comparisons often require untestable assumptions, a sensitivity analysis is warranted. We show how this can be performed in a meaningful and practically useful way. Following the approach of Molenberghs, Kenward and Goetghebeur in a missing data context, we evaluate the separate contributions of structural uninformativeness and sampling variation to uncertainty about the population parameters. This leads us to consider Honestly Estimated Ignorance Regions (HEIRs) and Estimated Uncertainty RegiOns (EUROs), respectively. We use the results to estimate the causal effect of observed exposure on successful blood pressure reduction in a randomized controlled clinical trial with partial non-compliance.