Evidence map›Paper›PMID 29504289›Full record

ArticleResearch synthesis methods2018

Methods for evidence synthesis in the case of very few studies.

Ralf Bender, Tim Friede, Armin Koch, Oliver Kuss, Peter Schlattmann, Guido Schwarzer, Guido Skipka

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research synthesis methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 55 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 55 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

114 citing papers in PubMed, 55 syntheses or guidelines pooled it.

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  3. Personality domains in early stages of psychosis: a systematic review and meta-analysis.European archives of psychiatry and clinical neuroscience · 2026
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54 more citing papers are in PubMed but not listed here.

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

7 authors.

Ralf BenderDepartment of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany.ORCID http://orcid.org/0000-0002-2422-4362
Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Armin KochInstitute for Biostatistics, Hannover Medical School, Hannover, Germany.
Oliver KussInstitute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany.
Peter SchlattmannInstitute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany.
Guido SchwarzerInstitute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.ORCID http://orcid.org/0000-0001-6214-9087
Guido SkipkaDepartment of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany.

Funding

EU FP HEALTH 2013602144
6 · The paper itself

Abstract

In systematic reviews, meta-analyses are routinely applied to summarize the results of the relevant studies for a specific research question. If one can assume that in all studies the same true effect is estimated, the application of a meta-analysis with common effect (commonly referred to as fixed-effect meta-analysis) is adequate. If between-study heterogeneity is expected to be present, the method of choice is a meta-analysis with random effects. The widely used DerSimonian and Laird method for meta-analyses with random effects has been criticized due to its unfavorable statistical properties, especially in the case of very few studies. A working group of the Cochrane Collaboration recommended the use of the Knapp-Hartung method for meta-analyses with random effects. However, as heterogeneity cannot be reliably estimated if only very few studies are available, the Knapp-Hartung method, while correctly accounting for the corresponding uncertainty, has very low power. Our aim is to summarize possible methods to perform meaningful evidence syntheses in the situation with only very few (ie, 2-4) studies. Some general recommendations are provided on which method should be used when. Our recommendations are based on the existing literature on methods for meta-analysis with very few studies and consensus of the authors. The recommendations are illustrated by 2 examples coming from dossier assessments of the Institute for Quality and Efficiency in Health Care.

Indexed as

Computer SimulationData Interpretation, StatisticalMeta-Analysis as TopicUncertaintyAbataceptAlgorithmsBayes TheoremCyclosporineFemaleHumansImmunosuppressive AgentsKidney TransplantationMaleModels, StatisticalNeoplasm MetastasisProstatic Neoplasms, Castration-ResistantAbataceptCyclosporineImmunosuppressive Agentscommon effectevidence synthesisfixed effectsmeta-analysisrandom effectsvery few studies

Identifiers

PMID29504289
PMCPMC6175308

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.