Evidence map›Paper›PMID 27754556›Full record

SynthesisBiometrical journal. Biometrische Zeitschrift2017

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

Tim Friede, Christian Röver, Simon Wandel, Beat Neuenschwander

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

Synthesis in Biometrical journal. Biometrische Zeitschrift, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 18 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

46 citing papers in PubMed, 18 syntheses or guidelines pooled it.

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  12. Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors.International journal of environmental research and public health · 2021
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  13. Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges.International journal of environmental research and public health · 2021
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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.

Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073, Göttingen, Germany.
Christian RöverDepartment of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073, Göttingen, Germany.
Simon WandelNovartis Pharma AG, Oncology, 4002, Basel, Switzerland.
Beat NeuenschwanderNovartis Pharma AG, Oncology, 4002, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes.

Indexed as

Models, StatisticalRare DiseasesBayes TheoremComputer SimulationUncertaintyBayesian statisticsBetween-study heterogeneityCoverage probabilityOrphan diseaseRandom-effects meta-analysis

Identifiers

PMID27754556
PMCPMC5516158

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.