A Bayesian hierarchical model for individual participant data meta-analysis of demand curves

Shengwei Zhang, Haitao Chu, Warren K. Bickel, Chap T. Le, Tracy T. Smith, Janet L. Thomas, Eric C. Donny, Dorothy K. Hatsukami, Xianghua Luo

Research output: Contribution to journalArticlepeer-review

Abstract

Individual participant data meta-analysis is a frequently used method to combine and contrast data from multiple independent studies. Bayesian hierarchical models are increasingly used to appropriately take into account potential heterogeneity between studies. In this paper, we propose a Bayesian hierarchical model for individual participant data generated from the Cigarette Purchase Task (CPT). Data from the CPT details how demand for cigarettes varies as a function of price, which is usually described as an exponential demand curve. As opposed to the conventional random-effects meta-analysis methods, Bayesian hierarchical models are able to estimate both the study-specific and population-level parameters simultaneously without relying on the normality assumptions. We applied the proposed model to a meta-analysis with baseline CPT data from six studies and compared the results from the proposed model and a two-step conventional random-effects meta-analysis approach. We conducted extensive simulation studies to investigate the performance of the proposed approach and discussed the benefits of using the Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

Original languageEnglish (US)
Pages (from-to)2276-2290
Number of pages15
JournalStatistics in Medicine
Volume41
Issue number12
DOIs
StatePublished - May 30 2022

Bibliographical note

Funding Information:
This research was funded by the National Institute of Health's National Cancer Institute grant P01CA217806, National Heart, Lung, and Blood Institute grant R01HL094183, National Institute of on Drug Abuse/Food and Drug Administration grant U54DA031659, National Center for Advancing Translational Sciences grant UL1TR002494, and the National Library of Medicine grants R21LM012744 and R01LM012982. The authors thank Mr. Bruce Lindgren, Ms. Qing Cao, and Ms. Katelyn Tessier for preparing the data, which was carried out in the Biostatistics and Bioinformatics Shared Resources of the Masonic Cancer Center, supported in part by the National Cancer Institute Cancer Center Support grant P30CA077598. Data sharing is not applicable to this article as no new data were generated in this study. The authors would like to thank the two referees and the Associate Editor for their constructive comments which have helped improve the manuscript greatly.

Funding Information:
information National Cancer Institute, P01CA217806; P30CA077598; National Heart, Lung, and Blood Institute, R01HL094183; National Institute of on Drug Abuse/Food and Drug Administration, U54DA031659; U.S. National Library of Medicine, R01LM01298; R21LM012744This research was funded by the National Institute of Health's National Cancer Institute grant P01CA217806, National Heart, Lung, and Blood Institute grant R01HL094183, National Institute of on Drug Abuse/Food and Drug Administration grant U54DA031659, National Center for Advancing Translational Sciences grant UL1TR002494, and the National Library of Medicine grants R21LM012744 and R01LM012982. The authors thank Mr. Bruce Lindgren, Ms. Qing Cao, and Ms. Katelyn Tessier for preparing the data, which was carried out in the Biostatistics and Bioinformatics Shared Resources of the Masonic Cancer Center, supported in part by the National Cancer Institute Cancer Center Support grant P30CA077598. Data sharing is not applicable to this article as no new data were generated in this study. The authors would like to thank the two referees and the Associate Editor for their constructive comments which have helped improve the manuscript greatly.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Keywords

  • Bayesian hierarchical model
  • cigarette purchase task
  • demand curves
  • meta-analysis
  • Tobacco Products
  • Humans
  • Bayes Theorem
  • Data Analysis

PubMed: MeSH publication types

  • Meta-Analysis
  • Journal Article

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