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Precision Bayesian phase I-II dose-finding based on utilities tailored to prognostic subgroups.

Juhee Lee1, Peter F Thall2, Pavlos Msaouel3

  • 1Department of Statistics, University of California, Santa Cruz, California, USA.

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This study introduces a Bayesian adaptive clinical trial design for optimizing drug dosage in metastatic clear cell renal carcinoma, considering patient subgroups and balancing toxicity and efficacy for personalized treatment.

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Bayesian phase I-II clinical trial designadaptive randomizationclusteringdose findingpatient prognostic subgroups

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Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Oncology

Background:

  • Metastatic clear cell renal carcinoma (mccRCC) treatment requires optimized dosing strategies.
  • Patient stratification using prognostic scores is crucial for targeted therapies.
  • Balancing drug toxicity and efficacy is a key challenge in early-phase trials.

Purpose of the Study:

  • To develop a Bayesian phase I-II adaptive design for dose optimization in mccRCC.
  • To integrate prognostic subgroups and dual clinical outcomes (toxicity and efficacy) into the design.
  • To facilitate information sharing across subgroups with similar dose-outcome profiles.

Main Methods:

  • A joint probability model was constructed for time-to-toxicity and ordinal disease status.
  • Adaptive clustering of subgroups with similar dose-outcome distributions was employed.
  • Subgroup-specific utility functions quantified toxicity-efficacy trade-offs.

Main Results:

  • A novel Bayesian adaptive design was constructed for mccRCC trials.
  • Simulation studies demonstrated the design's reliability, safety, and robustness.
  • The proposed design outperformed methods that ignore or separate subgroups.

Conclusions:

  • The developed Bayesian design effectively optimizes drug dosage within prognostic subgroups for mccRCC.
  • Adaptive clustering and subgroup-specific utilities enhance information sharing and risk-benefit assessment.
  • This approach offers a more efficient and personalized strategy for targeted cancer therapy trials.