Estimating the treatment effect for adherers using multiple imputation

TL;DR

This paper introduces a multiple imputation approach to estimate treatment effects for adherers in clinical trials, simplifying complex calculations and providing reliable confidence intervals, demonstrated through simulations and real data application.

Contribution

It develops a novel MI-based method for estimating adherer effects, making the approach more accessible and computationally feasible compared to previous complex methods.

Findings

MI estimators are consistent and have correct coverage probabilities

The method performs well in simulation studies

Applied successfully to real clinical trial data

Abstract

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.