  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 used 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. No variance
estimation formula is provided, however, due to the complexity of the
estimators. In addition, it is difficult to evaluate the performance of the
bootstrap confidence interval (CI) due to computational intensity in the
complex estimation procedure. The current research implements estimators for
AdACE using multiple imputation (MI) and constructs CI through bootstrapping. A
simulation study shows that the MI-based estimators provide consistent
estimators with nominal coverage probability of CIs for the treatment
difference for the adherent populations of interest. Application to a real
dataset is illustrated by comparing two basal insulins for patients with type 1
diabetes.
