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Some Common Dose-Exposure-Response Estimands and Conditions for Their Causal Identifiability.

Christian Bartels1, Yuchen Wang2, Jonathan French3

  • 1Novartis Pharma AG, Basel, Switzerland.

CPT: Pharmacometrics & Systems Pharmacology
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

Applying the estimand framework to dose-exposure-response (DER) analyses enhances drug development dose selection. This structured approach, integrating causal inference, improves the precision and interpretability of DER modeling results.

Keywords:
causal inferencedose‐findingestimand frameworkexposure–responsepharmacometricsstandardization

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Area of Science:

  • Pharmacometrics
  • Drug Development
  • Causal Inference

Background:

  • Exposure-response (ER) analyses are crucial for dose selection in drug development.
  • The estimand framework (ICH E9(R1)) provides precise scientific objective definition.
  • Standard ER analyses often lack formal integration with causal inference principles.

Purpose of the Study:

  • To apply the estimand framework to dose-exposure-response (DER) analyses.
  • To formalize dose-response estimands using potential outcome notation and directed acyclic graphs (DAGs).
  • To compare different estimation strategies for DER estimands.

Main Methods:

  • Simulated studies based on real-world scenarios were used.
  • Estimands were defined and formalized using potential outcome notation.
  • Directed acyclic graphs (DAGs) expressed study assumptions and relationships.
  • Three estimation approaches were derived: pooled DER, pooled covariate-adjusted DR, and cohort summaries.

Main Results:

  • Pooled dose-exposure-response (DER) analysis yielded more precise estimates than covariate-adjusted dose-response (DR) analysis (lower Mean Square Error).
  • The integrated approach allows for formal assessment of bias in different estimation methods.
  • Different estimation methods for the same estimand can be compared for validation.

Conclusions:

  • Integrating the estimand framework with causal inference enhances methodological rigor in DER analyses.
  • This approach improves the clarity of modeling assumptions and interpretation of results.
  • The generalizable strategy advances exposure-response analyses for precise dose selection, especially with multi-study data.