Evidence map›Paper›PMID 25541687›Full record

ReviewPloS one2014

Network meta-analysis using R: a review of currently available automated packages.

Binod Neupane, Danielle Richer, Ashley Joel Bonner, Taddele Kibret, Joseph Beyene

Erratum issuedAbstract readComparative StudyReview
PubMed Full text at PMC Publisher
In one paragraph

Review in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 167 papers, 84 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
167citing papers in PubMed, 84 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

167 citing papers in PubMed, 84 syntheses or guidelines pooled it.

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107 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Binod NeupaneDepartment of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
Danielle RicherDepartment of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
Ashley Joel BonnerDepartment of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
Taddele KibretDepartment of Mathematics and Statistics, McMaster University, HH 218, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
Joseph BeyeneDepartment of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1; Department of Mathematics and Statistics, McMaster University, HH 218, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

Network meta-analysis (NMA)--a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously--has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.

Indexed as

Data Interpretation, StatisticalSoftwareDiabetes MellitusHumansHypertensionIncidenceMeta-Analysis as Topic

Identifiers

PMID25541687
PMCPMC4277278

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.