Estimating the treatment effect for adherers using multiple imputation

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.