Ho Yen-Yi, Nhu Vo Tien, Chu Haitao, LeSage Mark G, Luo Xianghua, Le Chap T
1 Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA.
2 Division of Epidemiology, School of Public Health, University of Minnesota, MN, USA.
Stat Methods Med Res. 2018 Aug;27(8):2401-2412. doi: 10.1177/0962280216680651.
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.
药物自我给药实验是一种常用的方法，用于评估化合物的滥用可能性和强化特性。它已被用于评估各种物质的滥用可能性，如精神运动兴奋剂、致幻剂、食物、尼古丁和酒精。自我给药研究产生的需求曲线描述了药物或非药物强化物的需求如何随价格变化而变化。随着2009年《家庭吸烟预防与烟草控制法案》的批准，需求曲线分析为美国食品药品监督管理局的烟草监管政策提供了关键证据，因为它产生了几个重要的定量测量指标来评估尼古丁的强化强度。常用于分析需求曲线数据的传统方法是个体特异性非线性最小二乘回归。非线性最小二乘方法旨在使数据集中每个受试者的残差平方和最小化；然而，这种一次处理一个受试者的方法不允许在统一的模型框架中估计受试者间和受试者内的变异性。在本文中，我们回顾了分析需求曲线数据的现有方法、非线性最小二乘回归和混合效应回归，并提出了一种新的贝叶斯层次模型。我们进行模拟分析以比较这三种方法的性能，并在大鼠尼古丁自我给药的案例研究中说明所提出的方法。我们展示模拟结果并讨论使用所提出方法的好处。
