TY - JOUR
T1 - A Bayesian hierarchical model for demand curve analysis
AU - Ho, Yen Yi
AU - Vo, Tien N
AU - Chu, Haitao
AU - LeSage, Mark G.
AU - Luo, Xianghua
AU - Le, Chap T
N1 - Publisher Copyright:
© 2016, The Author(s) 2016.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - Drug self-administration experiments are a frequently used approach to assess 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.
AB - Drug self-administration experiments are a frequently used approach to assess 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.
KW - Bayesian hierarchical model
KW - demand curve analysis
KW - mixed effects regression
KW - non-linear least square regression
KW - prism
UR - http://www.scopus.com/inward/record.url?scp=85049874499&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049874499&partnerID=8YFLogxK
U2 - 10.1177/0962280216680651
DO - 10.1177/0962280216680651
M3 - Article
C2 - 29984638
AN - SCOPUS:85049874499
SN - 0962-2802
VL - 27
SP - 2401
EP - 2412
JO - Statistical methods in medical research
JF - Statistical methods in medical research
IS - 8
ER -