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April 16, 2026CPT Pharmacometrics & Systems Pharmacology0 citationsOpen Access

Objective First, Method Second: Why the Estimand Definition Comes First in Pharmacometric Covariate Modeling

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EJE. Niclas JonssonJNJoakim NybergFMFrance Mentré

Key Points

  • This research aims to clarify the significance of defining estimands before choosing estimators in pharmacometric modeling.
  • Analyzed the ICH E9(R1) estimand framework
  • Distinguished between conditional and unconditional covariate effects
  • Emphasized the importance of objective first in covariate modeling
  • Enhanced understanding of the mechanistic and regulatory needs in pharmacometrics
  • Establishing that defining the estimand is crucial before determining the estimator
  • Improved communication of clinical impacts and optimized patient care strategies

Abstract

Pharmacometric covariate analyses support both mechanistic understanding and clinical decision-making, that is, two different objectives which require different analysis targets. Adopting the ICH E9(R1) estimand framework clarifies this distinction, reconciling mechanistic modeling (conditional covariate effects) with regulatory and clinical needs (unconditional covariate effects), clarifying that estimand definition, i.e. the objective, and not the estimator, is the first choice to make in covariate modeling. This structured approach optimizes quantitative evidence for communicating clinical impact and patient care.

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Cite This Study

Jonsson et al. (2026) studied this question.

synapsesocial.com/papers/69e07c972f7e8953b7cbdd3chttps://doi.org/10.1002/psp4.70251
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  1. 1Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies2023 · 21 citations
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  5. 5Covariate modeling in pharmacometrics: General points for consideration2024 · 23 citations