Evidence map›Paper›PMID 35194829›Full record

SynthesisStatistics in medicine2022

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

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shengwei ZhangDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Haitao ChuDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Warren K BickelAddiction Recovery Research Center, Virginia Tech Carilion Research Institute, Roanoke, Virginia, USA.
Chap T LeDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Tracy T SmithDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
Janet L ThomasDivision of General Internal Medicine, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
Eric C DonnyBaptist Comprehensive Cancer Center and Department of Physiology and Pharmacology, School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA.
Dorothy K HatsukamiDepartment of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA.
Xianghua LuoDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.ORCID 0000-0001-7501-6582

Funding

Women's CancerP30CA077598 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Timothy C. Hallstrom · 1998 to 2026
$100.4M
VERY LOW NICOTINE CIGARETTES IN SMOKERS WITH SCHIZOPHRENIAU54DA031659 · NIDA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HECHT, STEPHEN S · 2011 to 2022
$64.7M
Trial Design and Biostatistical Support CoreP01CA065493 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Mark J Osborn · 1995 to 2026
$48.2M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
Project 4: Effects of cigarette filter ventilation on cigarette appeal and preferences for combusted and noncombusted tobacco productsP01CA217806 · NCI · UNIVERSITY OF MINNESOTA · PI HATSUKAMI, DOROTHY K, SHIELDS, PETER G. · 2017 to 2022
$12.6M
Enhancing Quit and Win Contests to Improve Cessation Among College SmokersR01HL094183 · NHLBI · UNIVERSITY OF MINNESOTA · PI THOMAS, JANET L · 2009 to 2013
$3.7M
Statistical Methods and Software for Multivariate Meta-analysisR01LM012982 · NLM · UNIVERSITY OF MINNESOTA · PI LIN, LIFENG, SIEGEL, LIANNE · 2019 to 2022
$1.3M
Joint Meta-Regression Methods Accounting for Postrandomization VariablesR21LM012744 · NLM · UNIVERSITY OF MINNESOTA · PI CHU, HAITAO · 2017 to 2018
$376k
NCATS NIH HHS UL1 TR002494NCI NIH HHS P01 CA065493NCI NIH HHS P01 CA217806NCI NIH HHS P01CA217806NCI NIH HHS P30 CA077598NCI NIH HHS P30CA077598NHLBI NIH HHS R01 HL094183NHLBI NIH HHS R01HL094183NIDA NIH HHS U54 DA031659NIDA NIH HHS U54DA031659NLM NIH HHS R01LM01298NLM NIH HHS R01 LM012982NLM NIH HHS R21 LM012744NLM NIH HHS R21LM012744
6 · The paper itself

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.

Indexed as

Tobacco ProductsBayes TheoremData AnalysisHumansBayesian hierarchical modelcigarette purchase taskdemand curvesmeta-analysis

Identifiers

PMID35194829
PMCPMC9035095

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.