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Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Stereoacuity Improvement using Random-Dot Video Games
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Improved adaptive randomization strategies for a seamless Phase I/II dose-finding design.

Donglin Yan1, Nolan A Wages2, Emily V Dressler3

  • 1a Department of biostatistics, College of Public Health , University of Kentucky , KY , USA.

Journal of Biopharmaceutical Statistics
|November 20, 2018
PubMed
Summary

This study introduces new randomization methods for seamless Phase I/II cancer trials, improving optimal dose selection and patient allocation. The recommended strategy enhances accuracy and efficiency without increasing toxicity risk.

Keywords:
Seamless I/II adaptive designadaptive randomizationcontinual reassessment methoddose findingmolecularly targeted agentoptimal biological dose

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacology

Background:

  • Seamless Phase I/II designs are crucial for efficient dose-finding in cancer drug development.
  • Existing adaptive randomization (AR) methods may not optimally balance dose-efficacy assumptions, especially for molecularly targeted agents.
  • The Wages and Tait (2015) design, while innovative, has limitations in model selection and sample size specification for AR.

Purpose of the Study:

  • To propose and evaluate novel randomization strategies for the AR stage in seamless Phase I/II dose-finding designs.
  • To enhance the estimation of optimal doses and improve patient allocation to effective treatments.
  • To address limitations of the original Wages and Tait design, including arbitrary sample size requirements.

Main Methods:

  • Developed three alternative randomization strategies calculating probabilities based on candidate model likelihood.
  • Compared proposed methods against the original design using extensive simulations.
  • Evaluated performance based on optimal dose estimation accuracy and patient allocation to effective doses.

Main Results:

  • The proposed methods, particularly the recommended strategy, demonstrated superior performance in simulations.
  • The recommended method improved patient allocation to the optimal dose across most scenarios.
  • Enhanced accuracy in selecting the final optimal dose was observed without a significant increase in toxicity.

Conclusions:

  • The proposed randomization strategies offer a significant improvement over existing AR methods in seamless Phase I/II dose-finding.
  • The recommended strategy provides a more robust and efficient approach to dose escalation and de-escalation.
  • These findings have implications for optimizing cancer clinical trial designs and accelerating drug development.