  Randomized controlled trials are considered the gold standard to evaluate the
treatment effect (estimand) for efficacy and safety. According to the recent
International Council on Harmonisation (ICH)-E9 addendum (R1), intercurrent
events (ICEs) need to be considered when defining an estimand, and principal
stratum is one of the five strategies to handle ICEs. Qu et al. (2020,
Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the
adherer average causal effect (AdACE) for estimating the treatment difference
for those who adhere to one or both treatments based on the causal-inference
framework, and demonstrated the consistency of those estimators; however, this
method requires complex custom programming related to high-dimensional numeric
integrations. In this article, we implemented the AdACE estimators using
multiple imputation (MI) and constructs CI through bootstrapping. A simulation
study showed that the MI-based estimators provided consistent estimators with
the nominal coverage probabilities of CIs for the treatment difference for the
adherent populations of interest. As an illustrative example, the new method
was applied to data from a real clinical trial comparing 2 types of basal
insulin for patients with type 1 diabetes.
