Evidence map›Paper›PMID 35316455›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2022

Using Bayesian Meta-Regression to Advance Prevention Science Research: an Introduction and Empirical Illustration.

Christopher G Thompson, Brandie Semma, Naike Wang, Idean Ettekal

Abstract read
PubMed Publisher
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Advantages in Bayesian Approaches to Confirmatory Factor Analysis.Prevention science : the official journal of the Society for Prevention Research · 2026
    Article
  2. Article
  3. Leveraging Research Synthesis Methods to Support Evidence-Based Policy- and Decision-Making.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
  4. Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
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

4 authors.

Christopher G ThompsonDepartment of Educational Psychology, Texas A&M University, College Station, TX, 77840, USA. cgthompson@tamu.edu.
Brandie SemmaDepartment of Educational Psychology, Texas A&M University, College Station, TX, 77840, USA.
Naike WangDepartment of Educational Psychology, Texas A&M University, College Station, TX, 77840, USA.
Idean EttekalDepartment of Educational Psychology, Texas A&M University, College Station, TX, 77840, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Driven by the movement of evidence-based practices, Bayesian statistical methods have become increasingly popular. This paper introduces a Bayesian approach to meta-regression, focusing on the use and implementation in prevention science research. We first compare Bayesian meta-analysis and meta-regression to a frequentist approach. Thereafter, we illustrate Bayesian methods in meta-regression, highlighting advantages, providing detailed interpretation, and presenting results. The example is completed using several R packages. We also provide annotated R code for readers as a foundation for their own research.

Indexed as

Health Services ResearchBayes TheoremHumansMeta-Analysis as TopicBayesian methodsMeta-analysisMeta-regression

Identifiers

PMID35316455

What OpenQuestion holds

Textmetadata
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.