Network meta-analysis using R: a review of currently available automated packages.
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 imple...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4277278?pdf=render |
| _version_ | 1818050887602405376 |
|---|---|
| author | Binod Neupane Danielle Richer Ashley Joel Bonner Taddele Kibret Joseph Beyene |
| author_facet | Binod Neupane Danielle Richer Ashley Joel Bonner Taddele Kibret Joseph Beyene |
| author_sort | Binod Neupane |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T11:00:37Z |
| format | Article |
| id | doaj.art-72f080b8080341cdb0f2f83ea9a56ca4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T11:00:37Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-72f080b8080341cdb0f2f83ea9a56ca42022-12-22T01:51:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11506510.1371/journal.pone.0115065Network meta-analysis using R: a review of currently available automated packages.Binod NeupaneDanielle RicherAshley Joel BonnerTaddele KibretJoseph BeyeneNetwork 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.http://europepmc.org/articles/PMC4277278?pdf=render |
| spellingShingle | Binod Neupane Danielle Richer Ashley Joel Bonner Taddele Kibret Joseph Beyene Network meta-analysis using R: a review of currently available automated packages. PLoS ONE |
| title | Network meta-analysis using R: a review of currently available automated packages. |
| title_full | Network meta-analysis using R: a review of currently available automated packages. |
| title_fullStr | Network meta-analysis using R: a review of currently available automated packages. |
| title_full_unstemmed | Network meta-analysis using R: a review of currently available automated packages. |
| title_short | Network meta-analysis using R: a review of currently available automated packages. |
| title_sort | network meta analysis using r a review of currently available automated packages |
| url | http://europepmc.org/articles/PMC4277278?pdf=render |
| work_keys_str_mv | AT binodneupane networkmetaanalysisusingrareviewofcurrentlyavailableautomatedpackages AT daniellericher networkmetaanalysisusingrareviewofcurrentlyavailableautomatedpackages AT ashleyjoelbonner networkmetaanalysisusingrareviewofcurrentlyavailableautomatedpackages AT taddelekibret networkmetaanalysisusingrareviewofcurrentlyavailableautomatedpackages AT josephbeyene networkmetaanalysisusingrareviewofcurrentlyavailableautomatedpackages |