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Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection

Martin Otava1, Ziv Shkedy1, Ludwig A Hothorn2

  • 1a Interuniversity Institute for Biostatistics and Statistical Bioinformatics , Hasselt University , Hasselt , Belgium.

Journal of Biopharmaceutical Statistics
|March 23, 2017
PubMed
Summary
This summary is machine-generated.

Identifying the minimum effective dose is crucial for drug development. A Bayesian variable selection approach offers an alternative to information criteria for selecting this dose, accounting for model uncertainty.

Keywords:
Bayesian variable selectionminimum effective dosemodel selectionmodel uncertaintyorder restricted models

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Area of Science:

  • Pharmacology and Biostatistics
  • Drug Development and Statistical Modeling

Background:

  • Determining the minimum effective dose (MED) is critical in pharmaceutical research and development.
  • Current methods often rely on model selection using information criteria, which may not fully address model uncertainty.

Purpose of the Study:

  • To introduce and evaluate a Bayesian variable selection approach for identifying the minimum effective dose.
  • To compare the performance of this Bayesian method against the generalized order restricted information criterion (GORIC).

Main Methods:

  • The study employed a Bayesian variable selection technique.
  • Performance was assessed using two real-world dose-response experiments.
  • Simulations were conducted to further compare the methods under various conditions.

Main Results:

  • The Bayesian variable selection approach effectively identifies the minimum effective dose while considering model uncertainty.
  • The comparative performance of Bayesian variable selection and GORIC is contingent upon factors like model complexity and signal-to-noise ratio.
  • The Bayesian method demonstrated robust performance in dose-response analysis.

Conclusions:

  • Bayesian variable selection provides a valuable alternative for minimum effective dose identification in early drug screening.
  • The choice between Bayesian methods and information criteria depends on specific experimental characteristics and desired outcomes.