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Related Experiment Video

Updated: Jan 18, 2026

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Estimands for Early-Phase Dose Optimization Trials in Oncology.

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|September 10, 2025
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Summary

This study proposes using the estimand framework to define the optimal biological dose (OBD) in oncology trials, balancing efficacy and toxicity. It clarifies objectives and analysis for early-phase dose optimization studies.

Keywords:
Bayesian adaptive designsProject Optimusestimandsintercurrent eventsoncology

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Area of Science:

  • Clinical Trials
  • Pharmacology
  • Biostatistics

Background:

  • Traditional Phase I oncology trials focus on maximum tolerated dose.
  • Novel therapies like targeted agents show fewer dose-limiting toxicities, necessitating the optimal biological dose (OBD) concept.
  • The ICH E9(R1) estimand framework enhances trial clarity but lacks specific guidance for early-phase dose optimization.

Purpose of the Study:

  • To discuss the application of the estimand framework in oncology dose optimization trials.
  • To integrate efficacy and toxicity data using utility functions for OBD determination.
  • To provide recommendations for implementing the estimand framework in early-phase oncology studies.

Main Methods:

  • Utilizing utility functions to balance efficacy and toxicity for optimal dose selection.
  • Incorporating pharmacokinetic, toxicity, and efficacy data in the analysis.
  • Applying the estimand framework to define trial objectives and statistical analysis for dose optimization.

Main Results:

  • The proposed framework enhances clarity in dose optimization trial objectives.
  • It facilitates a better understanding of the drug's properties for selecting the correct dose for Phase II.
  • Recommendations for handling intercurrent events and defining treatment attributes in seamless trials are provided.

Conclusions:

  • The estimand framework, when applied to dose optimization, improves the definition of the optimal biological dose (OBD).
  • This approach ensures alignment between trial objectives and statistical methods, crucial for novel oncology therapeutics.
  • Clearer objectives and analysis strategies enable more efficient progression of promising drugs into later clinical development stages.