More powerful randomization-based p-values in double-blind trials with non-compliance.

Standard randomization-based tests of sharp null hypotheses in randomized clinical trials, that is, intent-to-treat analyses, are valid without extraneous assumptions, but generally can be appropriately powerful only with alternative hypotheses that involve treatment assignment having an effect on outcome. In the context of clinical trials with non-compliance, other alternative hypotheses can be more natural. In particular, when a trial is double-blind, it is often reasonable for the alternative hypothesis to exclude any effect of treatment assignment on outcome for a unit unless the assignment affected which treatment that unit actually received. Bayesian analysis under this alternative 'exclusion' hypothesis leads to new estimates of the effect of receipt of treatment, and to a new randomization-based procedure that has frequentist validity yet can be substantially more powerful than the standard intent-to-treat procedure. The key idea is to obtain a p-value using a posterior predictive check distribution, which includes a model for non-compliance behaviour, although only under the standard sharp null hypothesis of no effect of assignment (or receipt) of treatment on outcome. It is important to note that these new procedures are distinctly different from 'as treated' and 'per protocol' analyses, which are not only badly biased in general, but generally have very low power.