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  3. Interpreting The Estimand Framework From A Causal Inference Perspective.
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  3. Interpreting The Estimand Framework From A Causal Inference Perspective.

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Interpreting the Estimand Framework From a Causal Inference Perspective.

Jinghong Zeng1,2

  • 1Department of Statistics, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand, 86 15992428924.

Jmirx Med
|May 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The estimand framework clarifies treatment effects in clinical trials. This study interprets it using causal inference, showing how intercurrent events mediate effects and strengthening the framework's foundation.

Keywords:
causal inferenceclinical trialestimandintercurrent eventtreatment effect

Related Experiment Videos

Area of Science:

  • Pharmaceutical Sciences
  • Biostatistics
  • Clinical Trials

Background:

  • The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) published the estimand framework in 2019 to standardize treatment effect definitions in clinical trials.
  • The estimand framework defines an estimand using five attributes: treatments, variables, target populations, population-level summaries, and intercurrent events.
  • Intercurrent events are handled using strategies like treatment policy, hypothetical, composite variable, while-on-treatment, and principal stratum.

Purpose of the Study:

  • To interpret the ICH estimand framework through the lens of causal inference.
  • To elucidate the distinctions between estimands and genuine causal treatment effects.
  • To propose a novel method for integrating causal inference principles into the estimand framework.

Main Methods:

  • Utilized a causal inference framework based on potential outcomes to define individual treatment effects (ITE) and average treatment effects (ATE).
  • Compared the statistical presentation of ATE with the attributes of an estimand.
  • Proposed mapping estimand attributes onto the statistical presentation of ATE, with intercurrent events acting as mediation mechanisms.

Main Results:

  • The statistical presentation of ATE is not equivalent to an estimand, primarily due to the handling of intercurrent events.
  • Intercurrent events influence the statistical presentation of ATE by modifying treatments, variables, and target populations.
  • The proposed mapping provides a novel way to integrate causal inference into the estimand framework, enhancing its theoretical foundation.

Conclusions:

  • Interpreting the estimand framework using causal inference offers a stronger theoretical basis for its application in clinical trials.
  • This causal perspective is valuable for both academic and pharmaceutical industry clinical trials.
  • The insights may also benefit observational studies seeking to understand causal inference theories.