Evidence map›Paper›PMID 29092392›Full record

ReviewEpidemiology and health2017

Network meta-analysis: application and practice using Stata.

Sungryul Shim, Byung-Ho Yoon, In-Soo Shin, Jong-Myon Bae

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Epidemiology and health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 344 papers, 128 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
344citing papers in PubMed, 128 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

344 citing papers in PubMed, 128 syntheses or guidelines pooled it.

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284 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

4 authors.

Sungryul ShimInstitute for Clinical Molecular Biology Research, Soonchunhyang University Hospital, Seoul, Korea.ORCID 0000-0003-4143-7383
Byung-Ho YoonDepartment of Orthopaedic Surgery, Seoul Paik Hospital, ,Inje University College of Medicine, Seoul, Korea.ORCID 0000-0001-8518-6331
In-Soo ShinDepartment of Education, Jeonju University, Jeonju, Korea.ORCID 0000-0001-7535-3511
Jong-Myon BaeDepartment of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea.ORCID 0000-0003-3080-7852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

Indexed as

Network Meta-Analysis as TopicSoftwareBiostatisticsHumansTreatment OutcomeBiostatisticMixed treatment comparisonNetwork meta-analysisTreatment outcome

Identifiers

PMID29092392
PMCPMC5733388

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.