Detecting Outliers in Network Meta-Analysis

A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.


*** NMAoutlier, version 0.1.13 ***

  • added n_cores argument to limit cores to 2 for small toy examples that run in < 5 sec for CRAN submission

*** NMAoutlier, version 0.1.12 ***

  • added maintainer to the description file

*** NMAoutlier, version 0.1.11 ***

  • DESCRIPTION: ORCIDs added; help pages updated (fix error for keratosis);datasets renamed

*** NMAoutlier, version 0.1.10 ***

  • added small examples for CRAN submission

*** NMAoutlier, version 0.1.7 ***

  • new datasets Laparoscopic and Gupta2013 added

*** NMAoutlier, version 0.1.6 ***

  • changed matplot to ggplot2 for the graphs of fwdplot and fwdplotest

*** NMAoutlier, version 0.1.5 ***

  • added logo
  • added README file
  • added data (Dias 2013)
  • deleted Indices function and implemented a solution inline where needed

*** NMAoutlier, version 0.1.4 ***

  • performance improvements for Subset
  • removed the Subset function and integrated the code to the InitialSubset file.

*** NMAoutlier, version 0.1.3 ***

  • initial release on GitHub

Reference manual

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0.1.18 by Maria Petropoulou, 2 months ago

Browse source code at

Authors: Maria Petropoulou [aut, cre] , Guido Schwarzer [aut] , Agapios Panos [aut] , Dimitris Mavridis [aut]

Documentation:   PDF Manual  

Task views: Meta-Analysis

GPL (>= 2) license

Imports netmeta, meta, stats, parallel, MASS, reshape2, ggplot2, gridExtra

See at CRAN