Give Your Treatment Effect a Meaning: Applying the ICH E9 (R1) Estimand Framework to Internet-based Interventions

The ICH E9(R1) Addendum on Estimands and Sensitivity Analysis provides a rigorous framework for clearly defining the treatment effect a clinical trial aims to estimate—the so-called estimand. Although the addendum has been widely adopted in pharmaceutical research, it remains underutilized in trials investigating internet-based interventions (IBIs). This is a missed opportunity, as the estimand framework can enhance the interpretability of IBI trials by minimizing the risk of generating estimates—empirical quantifications of the estimand derived from trial data—that are misaligned with the study’s objectives. When data collection and analysis strategies are not properly aligned with the estimand, the resulting estimates may diverge substantially from what researchers originally intended. In the worst case, the estimate may not only be disconnected from the intended estimand, but may quantify the treatment effect for a clinically irrelevant scenario, ultimately failing to answer the trial’s core clinical question. Avoiding such misalignment is essential. Applying the principles outlined in the addendum provides a structured approach to ensure consistency between trial design, analysis, and interpretation. Therefore, this manuscript introduces the ICH E9(R1) addendum to IBI researchers by (1) explaining estimands and their five defining attributes, (2) exploring how intercurrent events affect interpretation and how to handle them, (3) relating estimands to analysis strategies like intention-to-treat and per-protocol, and (4) demonstrating their application in two exemplary trials. We discuss how estimands guide study design, analysis, and reporting, offering practical recommendations for IBI research. We conclude that estimands are essential for improving the validity and transparency of digital health intervention research.