Evidence map›Paper›PMID 24223435›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2013

Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis.

David Lunn, Jessica Barrett, Michael Sweeting, Simon Thompson

Abstract read
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 7 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

29 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis.International journal of environmental research and public health · 2021
    Pooled it
  7. Pooled it
  8. Observational
  9. Article
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  11. Review
  12. Demographic responses of nearly extirpated endangered mountain caribou to recovery actions in Central British Columbia.Ecological applications : a publication of the Ecological Society of America · 2022
    Article
  13. Interoperability of statistical models in pandemic preparedness: principles and reality.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.Ecological applications : a publication of the Ecological Society of America · 2022
    Review
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  19. Article
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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.

David LunnMedical Research Council Biostatistics Unit Cambridge, UK.
Jessica Barrett
Michael Sweeting
Simon Thompson

Funding

British Heart Foundation RG/08/014/24067Department of Health 08/30/02Medical Research Council MC_EX_G0902100Medical Research Council MC_U105232027Medical Research Council MC_U105260792Medical Research Council MC_U105261167Medical Research Council MR/K014811/1Medical Research Council MR/L003120/1Medical Research Council MR/L501566/1
6 · The paper itself

Abstract

Meta-analysis is often undertaken in two stages, with each study analysed separately in stage 1 and estimates combined across studies in stage 2. The study-specific estimates are assumed to arise from normal distributions with known variances equal to their corresponding estimates. In contrast, a one-stage analysis estimates all parameters simultaneously. A

Indexed as

Abdominal aortic aneurysmBayesian hierarchical modellingBUGSMarkov chain Monte Carlo methodsRandom-effects meta-analysis

Identifiers

PMID24223435
PMCPMC3814003

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.