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A Bayesian dose-finding design for outcomes evaluated with uncertainty.

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This summary is machine-generated.

This study introduces a new method for phase I clinical trials to handle uncertain dose-limiting toxicity assessments. The approach improves the accuracy of identifying the maximum tolerated dose, benefiting patient safety and trial efficiency.

Keywords:
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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacology

Background:

  • Phase I clinical trials aim to determine safe drug dosages.
  • Assessing dose-limiting toxicity (DLT) is crucial but can be uncertain for some patients.
  • Existing methods may not fully account for this assessment uncertainty.

Purpose of the Study:

  • To develop a novel design for phase I trials that incorporates uncertainty in DLT assessment.
  • To adapt the popular continual reassessment method (CRM) to handle partially observed DLT outcomes.
  • To improve the identification of the maximum tolerated dose (MTD) in the presence of assessment uncertainty.

Main Methods:

  • A new trial design is proposed, focusing on the CRM framework.
  • Physician-assessed probabilities of DLT are used for patients with uncertain outcomes.
  • Data augmentation techniques estimate posterior DLT probabilities using both fully and partially observed data.
  • A simulation study compares the proposed design against standard CRM and thresholding methods.

Main Results:

  • The proposed design demonstrated superior performance in simulations.
  • It showed a higher probability of correctly identifying the MTD.
  • It also led to more patients being treated at the MTD compared to other methods utilizing partially observed outcomes.

Conclusions:

  • Incorporating uncertainty in DLT assessment enhances the performance of CRM-based phase I trial designs.
  • The proposed method offers a more robust approach to dose escalation when DLT assessment is not definitive.
  • This can lead to more efficient and reliable determination of safe and effective drug doses.