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A Bayesian hierarchical model for demand curve analysis.

Yen-Yi Ho1, Tien Nhu Vo2, Haitao Chu3

  • 11 Department of Statistics, College of Arts and Sciences, University of South Carolina, South Carolina, SC, USA.

Statistical Methods in Medical Research
|May 31, 2018
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Summary
This summary is machine-generated.

Drug self-administration studies assess compound abuse liability. A new Bayesian model improves analysis of drug demand curves, offering better insights into nicotine

Keywords:
Bayesian hierarchical modeldemand curve analysismixed effects regressionnon-linear least square regressionprism

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Area of Science:

  • Pharmacology
  • Behavioral Neuroscience
  • Biostatistics

Background:

  • Drug self-administration experiments are vital for evaluating abuse liability and reinforcing properties of compounds.
  • Demand curve analysis quantifies how reinforcer demand changes with price, informing regulatory policy, particularly for tobacco products like nicotine.
  • Current analysis methods, like individual-specific non-linear least square regression, limit the estimation of variability within and between subjects.

Purpose of the Study:

  • To review existing methods for analyzing drug self-administration demand curve data.
  • To propose a novel Bayesian hierarchical model for demand curve analysis.
  • To compare the performance of the proposed Bayesian model against traditional non-linear least square and mixed effects regression approaches.

Main Methods:

  • Review of non-linear least square regression and mixed effects regression for demand curve analysis.
  • Development and proposal of a Bayesian hierarchical model.
  • Simulation analyses to compare the statistical performance of the three modeling approaches.
  • Application of the models to a case study involving nicotine self-administration in rats.

Main Results:

  • Simulation results demonstrate the performance characteristics of each analytical approach.
  • The proposed Bayesian hierarchical model offers advantages in analyzing demand curve data.
  • Case study illustrates the practical application and benefits of the new model in nicotine research.

Conclusions:

  • The Bayesian hierarchical model provides a unified framework for estimating between- and within-subject variability in demand curve analysis.
  • This advanced statistical approach enhances the quantitative assessment of reinforcing strength for substances like nicotine.
  • The findings support the utility of Bayesian methods for informing tobacco regulatory policy and substance abuse research.