ArticleStatistical methods in medical research2018
A Bayesian hierarchical model for demand curve analysis.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The efficacy of medial patellofemoral ligament reconstruction combined with distal femoral varus osteotomy in recurrent patellar dislocation and genu valgum.Musculoskeletal surgery · 2025Pooled it
- Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros.Behavioural processes · 2022Article
- Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction.Perspectives on behavior science · 2021Article
- Behavioral economic demand as a unifying language for addiction science: Promoting collaboration and integration of animal and human models.Experimental and clinical psychopharmacology · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.