A Bayesian hierarchical model for demand curve analysis

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration’s policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Original languageEnglish (US)
Pages (from-to)2038-2049
Number of pages12
JournalStatistical methods in medical research
Volume27
Issue number7
DOIs
StatePublished - Jul 1 2018

Fingerprint

Bayesian Hierarchical Model
Self Administration
Least-Squares Analysis
Nicotine
Nonlinear Least Squares
Curve
Drugs
Least Squares Regression
Nonlinear Regression
Tobacco
Hallucinogens
United States Food and Drug Administration
Pharmaceutical Preparations
Mixed Effects
Smoking
Alcohols
Sum of squares
Food
Alcohol
Simulation

Keywords

  • Bayesian hierarchical model
  • demand curve analysis
  • mixed effects regression
  • non-linear least square regression
  • prism

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

Cite this

A Bayesian hierarchical model for demand curve analysis. / Ho, Yen Yi; Nhu Vo, Tien; Chu, Haitao; Luo, Xianghua; Le, Chap T.

In: Statistical methods in medical research, Vol. 27, No. 7, 01.07.2018, p. 2038-2049.

Research output: Contribution to journalArticle

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