R/NMAoutlier-package.R

In NMAoutlier: Detecting Outliers in Network Meta-Analysis

#' NMAoutlier: Brief overview of measures and methodologies for
#' detection of outlying and influential studies in network
#' meta-analysis.
#'
#' @description
#' R package \bold{NMAoutlier} provides methods and tools to detect
#' outlier and influential studies in network meta-analysis.
#'
#' @details
#' R package \bold{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
#' \bold{netmeta}.
#'
#' The \bold{NMAoutlier} package implements the following methods
#' described in Petropoulou (2020).
#' \itemize{
#'  \item \bold{Simple outlier and influential detection measures}
#'    (function \code{\link{NMAoutlier.measures}}):
#'  \enumerate{
#'    \item raw residuals,
#'    \item standardized residuals,
#'    \item studentized residuals,
#'    \item Mahalanobis distance,
#'    \item leverage;
#'   }
#' \item \bold{Outlier and influential detection measures considered
#'  study deletion (shift the mean)} (function
#'  \code{\link{NMAoutlier.measures}}):
#'  \enumerate{
#'    \item raw deleted residuals,
#'    \item standardized deleted residuals,
#'    \item studentized deleted residuals,
#'    \item Cook's distance,
#'    \item COVRATIO,
#'    \item weight leave one out,
#'    \item leverage leave one out,
#'    \item heterogeneity leave one out,
#'    \item R heterogeneity,
#'    \item R Qtotal,
#'    \item R Qheterogeneity,
#'    \item R Qinconsistency,
#'    \item DFBETAS;
#'  }
#' \item Plots of the several outlier and influential detection
#'   (simple and deletion) measures (function
#'   (\code{\link{measplot}}));
#' \item Q-Q plot for network meta-analysis (function
#'   \code{\link{Qnetplot}});
#' \item \bold{Forward Search algorithm in network meta-analysis}
#'   (function (\code{\link{NMAoutlier}})) based on Petropoulou et
#'   al. (2021);
#' \item forward plots (\code{\link{fwdplot}}) with monitoring
#'   statistics in each step of the FS algorithm:
#' \enumerate{
#'   \item P-scores (Rücker & Schwarzer, 2015),
#'   \item z-values for difference of direct and indirect evidence
#'     with back-calculation method (König et al., 2013; Dias et al.,
#'     2010),
#'   \item standardized residuals,
#'   \item heterogeneity variance estimator,
#'   \item Cook's distance,
#'   \item ratio of variances,
#'   \item Q statistics (Krahn et al., 2013);
#'   }
#' \item forward plots (\code{\link{fwdplotest}}) for summary
#'   treatment estimates in each iteration of the FS algorithm
#'   (Petropoulou et al., 2021).
#' }
#'
#' Type \code{help(package = "NMAoutlier")} for a listing of R functions
#' available in \bold{NMAoutlier}.
#'
#' Type \code{citation("NMAoutlier")} on how to cite \bold{NMAoutlier}
#' in publications.
#'
#' To report problems and bugs, please send an email to Dr. Maria
#' Petropoulou \email{petropoulou@imbi.uni-freiburg.de}.
#'
#' The development version of \bold{NMAoutlier} is available on GitHub
#' \url{https://github.com/petropouloumaria/NMAoutlier}.
#'
#' @name NMAoutlier-package
#'
#' @docType package
#'
#' @author Petropoulou Maria \email{petropoulou@imbi.uni-freiburg.de}.
#'
#' @references
#' Dias S, Welton NJ, Caldwell DM, Ades AE (2010):
#' Checking consistency in mixed treatment comparison meta-analysis.
#' \emph{Statistics in Medicine},
#' \bold{29}, 932--44
#'
#' König J, Krahn U, Binder H (2013):
#' Visualizing the flow of evidence in network meta-analysis and
#' characterizing mixed treatment comparisons.
#' \emph{Statistics in Medicine},
#' \bold{32}, 5414--29
#'
#' Krahn U, Binder H, König J (2013):
#' A graphical tool for locating inconsistency in network meta-analyses.
#' \emph{BMC Medical Research Methodology},
#' \bold{13}, 35
#'
#' Petropoulou M (2020):
#' Exploring methodological challenges in network meta-analysis models
#' and developing methodology for outlier detection.
#' \emph{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.
#' \emph{Statistics in Medicine}
#'
#' Rücker G (2012):
#' Network meta-analysis, electrical networks and graph theory.
#' \emph{Research Synthesis Methods},
#' \bold{3}, 312--24
#'
#' Rücker G, Schwarzer G (2015):
#' Ranking treatments in frequentist network meta-analysis works
#' without resampling methods.
#' \emph{BMC Medical Research Methodology},
#' \bold{15}, 58
#'
#' @keywords package
#'
#' @importFrom meta gs


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