Incorporating partial adherence into the principal stratification analysis framework

Participants in pragmatic clinical trials often partially adhere to treatment. In the presence of partial adherence, simple statistical analyses of binary adherence (receiving either full or no treatment) introduce biases. We developed a framework which expands the principal strati cation approach to allow partial adherers to have their own principal stratum and treatment level. We derived consistent estimates for bounds on population values of interest. A Monte Carlo posterior sampling method was derived that is computationally faster than Markov Chain Monte Carlo sampling, with con firmed equivalent results. Simulations indicate that the two methods agree with each other and are superior in most cases to the biased estimators created through standard principal strati cation. The results suggest that these new methods may lead to increased accuracy of inference in settings where study participants only partially adhere to assigned treatment.Science, Faculty ofStatistics, Department ofGraduat