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Considerations for master protocols using external controls.

Jie Chen1, Xiaoyun Nicole Li2, Chengxing Cindy Lu3

  • 1Data Sciences, ECR Global, Shanghai, China.

Journal of Biopharmaceutical Statistics
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Summary
This summary is machine-generated.

Master protocols accelerate oncology drug development using external controls. This paper details their types, considerations, and a causal roadmap for efficient treatment effect estimation in clinical trials.

Keywords:
External controlcausal roadmaphistorical controlreal-world estimandsynthetic controltargeted learningvirtual control

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Area of Science:

  • Clinical Trials Methodology
  • Oncology Drug Development
  • Biostatistics

Background:

  • Master protocols are increasingly used in oncology clinical trials for efficiency and flexibility.
  • These protocols (e.g., basket, umbrella, platform trials) study multiple drugs and subpopulations under one design.
  • Incorporating external controls can further enhance the efficiency of master protocols.

Purpose of the Study:

  • To provide an overview of external control types and their features in master protocols.
  • To discuss key considerations for using external controls in master protocols, including data assessment and estimand construction.
  • To present a causal roadmap for estimating treatment effects in master protocols with external controls.

Main Methods:

  • Review of different external control types and their application in master protocols.
  • Discussion of challenges and considerations, including fit-for-use real-world data and estimand definition.
  • Presentation of a three-step targeted learning-based causal roadmap: estimand definition, efficient estimation, and sensitivity analysis.

Main Results:

  • Identified various types of external controls and their unique characteristics within master protocols.
  • Outlined critical considerations for constructing estimands and assessing real-world data for external controls.
  • Demonstrated a causal roadmap for robust treatment effect estimation, supported by illustrative examples.

Conclusions:

  • Master protocols, especially when utilizing external controls, offer an efficient framework for oncology drug development.
  • Careful consideration of estimands, data quality, and causal assumptions is crucial for valid inference.
  • The proposed causal roadmap provides a structured approach to enhance the reliability of results from master protocols using external controls.