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

In NMAoutlier: Detecting Outliers in Network Meta-Analysis

Description

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

Details

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).

• Simple outlier and influential detection measures (function NMAoutlier.measures):

  1. raw residuals,

  2. standardized residuals,

  3. studentized residuals,

  4. Mahalanobis distance,

  5. leverage;

• Outlier and influential detection measures considered study deletion (shift the mean) (function NMAoutlier.measures):

  1. raw deleted residuals,

  2. standardized deleted residuals,

  3. studentized deleted residuals,

  4. Cook's distance,

  5. COVRATIO,

  6. weight leave one out,

  7. leverage leave one out,

  8. heterogeneity leave one out,

  9. R heterogeneity,

  10. R Qtotal,

  11. R Qheterogeneity,

  12. R Qinconsistency,

  13. DFBETAS;

• Plots of the several outlier and influential detection (simple and deletion) measures (function (measplot));

• Q-Q plot for network meta-analysis (function Qnetplot);

• Forward Search algorithm in network meta-analysis (function (NMAoutlier)) based on Petropoulou et al. (2021);

• forward plots (fwdplot) with monitoring statistics in each step of the FS algorithm:

  1. P-scores (Rücker & Schwarzer, 2015),

  2. z-values for difference of direct and indirect evidence with back-calculation method (König et al., 2013; Dias et al., 2010),

  3. standardized residuals,

  4. heterogeneity variance estimator,

  5. Cook's distance,

  6. ratio of variances,

  7. Q statistics (Krahn et al., 2013);

• forward plots (fwdplotest) for summary treatment estimates in each iteration of the FS algorithm (Petropoulou et al., 2021).

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 [e-mail].

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

Author(s)

Petropoulou Maria [e-mail].

References

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.