Evidence map›Paper›PMID 31476256›Full record

ArticleResearch synthesis methods2020

The dark side of the force: Multiplicity issues in network meta-analysis and how to address them.

Orestis Efthimiou, Ian R White

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research synthesis methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 9 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

21 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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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

2 authors.

Orestis EfthimiouInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0002-0955-7572
Ian R WhiteMRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK.

Funding

Medical Research Council MC_UU_12023/21Medical Research Council Unit Programme MC_UU_12023/21Swiss National Science Foundation (SNSF) 180083
6 · The paper itself

Abstract

Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multilevel Bayesian modelling, where treatment effects are modelled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model.

Indexed as

Research DesignAlgorithmsAntidepressive AgentsAntipsychotic AgentsBayes TheoremComputer SimulationData Interpretation, StatisticalFalse Positive ReactionsHumansModels, StatisticalNetwork Meta-Analysis as TopicSchizophreniaAntidepressive AgentsAntipsychotic Agentshierarchical modellingmixed treatment comparisonmultiple testingmultiple treatments

Identifiers

PMID31476256
PMCPMC7003789

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.