Evidence map›Paper›PMID 41923558›Full record

ArticleStatistics in medicine2026

Bayesian Random-Effects Meta-Analysis of Aggregate Data on Clinical Events.

Christian Röver, Qiong Wu, Anja Loos, Tim Friede

Abstract read
In one paragraph

Article in Statistics in medicine, 2026. 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

4 authors.

Christian RöverDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID https://orcid.org/0000-0002-6911-698X
Qiong WuDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID https://orcid.org/0009-0007-2942-8712
Anja LoosGlobal Biostatistics and Epidemiology, Merck KGaA, Darmstadt, Germany.
Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID https://orcid.org/0000-0001-5347-7441

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate intervention effects on rare events, meta-analysis techniques are commonly applied in order to assess the accumulated evidence. When it comes to adverse effects in clinical trials, these are often most adequately handled using survival methods. A common-effect model that is able to process data in commonly quoted formats in terms of hazard ratios has previously been proposed for this purpose. In order to accommodate potential heterogeneity between studies, we have extended the model by Holzhauer to a random-effects approach. The Bayesian model is described in detail, and applications to realistic data sets are discussed along with sensitivity analyses and Monte Carlo simulations to support the conclusions.

Indexed as

Meta-Analysis as TopicModels, StatisticalBayes TheoremClinical Trials as TopicComputer SimulationHumansMonte Carlo Methodheterogeneityhierarchical modelmeta‐analysisprior distribution

Identifiers

PMID41923558
PMCPMC13044572

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.