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Bayesian decision procedures based on logistic regression models for dose-finding studies

J Whitehead1, D Williamson

  • 1MPS Research Unit, The University of Reading, Earley Gate, UK.

Journal of Biopharmaceutical Statistics
|September 19, 1998
PubMed
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This study presents a Bayesian model to identify optimal experimental drug doses in early-phase clinical trials, aiming to minimize adverse event probabilities for patient safety. The flexible decision-theoretic procedure is suitable for standard statistical software.

Area of Science:

  • Clinical Trials Methodology
  • Bayesian Statistics
  • Drug Development

Background:

  • Early-phase clinical trials are crucial for determining safe and effective drug dosages.
  • Identifying doses with a low probability of adverse events is a primary objective.

Purpose of the Study:

  • To develop a Bayesian model and decision-theoretic procedure for optimal dose selection in early-phase trials.
  • To ensure patient safety by minimizing the likelihood of adverse events.

Main Methods:

  • A Bayesian statistical model was developed.
  • A decision-theoretic procedure was derived for sequential cohort dose selection.
  • Simulations were conducted to evaluate the procedure's properties.

Main Results:

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  • The presented procedure effectively identifies optimal doses for experimental drugs.
  • Simulations demonstrated the procedure's favorable characteristics in dose-finding studies.
  • The method is adaptable for use with common statistical software.

Conclusions:

  • The Bayesian decision-theoretic approach offers a flexible and robust method for early-phase clinical trial dose selection.
  • This approach aids in optimizing drug development by balancing efficacy and safety.
  • The procedure facilitates efficient and statistically sound dose determination.