NMAoutlier-package: NMAoutlier: Brief overview of measures and methodologies for...

Description Details Author(s) References


R package NMAoutlier provides methods and tools to detect outlier and influential studies in network meta-analysis.


R package NMAoutlier is a tool to detect outliers (studies with extreme results) and influential studies in network meta-analysis (Petropoulou, 2020). The package can calculate: simple outlier and influential measures; outlier and influential measures considered study deletion (shift the mean); the outlier detection methodology with Forward Search (FS) algorithm (Petropoulou et al., 2021). All proposed outlier and influential detection methods were fitted the frequentist NMA model by graph theory introduced by Rücker (2012) and implemented inR package netmeta.

The NMAoutlier package implements the following methods described in Petropoulou (2020).

Type help(package = "NMAoutlier") for a listing of R functions available in NMAoutlier.

Type citation("NMAoutlier") on how to cite NMAoutlier in publications.

To report problems and bugs, please send an email to Dr. Maria Petropoulou petropoulou@imbi.uni-freiburg.de.

The development version of NMAoutlier is available on GitHub https://github.com/petropouloumaria/NMAoutlier.


Petropoulou Maria petropoulou@imbi.uni-freiburg.de.


Dias S, Welton NJ, Caldwell DM, Ades AE (2010): Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29, 932–44

König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32, 5414–29

Krahn U, Binder H, König J (2013): A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology, 13, 35

Petropoulou M (2020): Exploring methodological challenges in network meta-analysis models and developing methodology for outlier detection. PhD dissertation

Petropoulou M, Salanti G, Rücker G, Schwarzer G, Moustaki I, Mavridis D (2021): A forward search algorithm for detecting extreme study effects in network meta-analysis. Statistics in Medicine

Rücker G (2012): Network meta-analysis, electrical networks and graph theory. Research Synthesis Methods, 3, 312–24

Rücker G, Schwarzer G (2015): Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Medical Research Methodology, 15, 58

NMAoutlier documentation built on Oct. 11, 2021, 5:23 p.m.