R/netmeta-package.R

In netmeta: Network Meta-Analysis using Frequentist Methods

#' netmeta: Brief overview of methods and general hints
#' 
#' @description
#' R package \bold{netmeta} (Balduzzi et al., 2023) provides
#' frequentist methods for network meta-analysis and supports
#' Schwarzer et al. (2015), Chapter 8 on network meta-analysis
#' \url{https://link.springer.com/book/10.1007/978-3-319-21416-0}.
#' 
#' @details
#' R package \bold{netmeta} is an add-on package for \bold{meta}
#' providing the following network meta-analysis models:
#' \itemize{
#' \item frequentist network meta-analysis (function
#'   \code{\link{netmeta}}) based on Rücker (2012) and Rücker &
#'   Schwarzer (2014);
#' \item additive network meta-analysis for combinations of treatments
#'   (\code{\link{netcomb}} for connected networks,
#'   \code{\link{discomb}} for disconnected networks) (Rücker et al.,
#'   2020a);
#' \item network meta-analysis of binary data
#'   (\code{\link{netmetabin}}) using the Mantel-Haenszel or
#'   non-central hypergeometric distribution method (Efthimiou et al.,
#'   2019).
#' }
#'
#' The following methods are available to present results of a network
#' meta-analysis:
#' \itemize{
#' \item network graphs (\code{\link{netgraph}}) described in Rücker &
#'   Schwarzer (2016);
#' \item forest plots (\code{\link{forest.netmeta}},
#'   \code{\link{forest.netcomb}});
#' \item league tables with network meta-analysis results
#'   (\code{\link{netleague}});
#' \item tables with network, direct and indirect estimates
#'   (\code{\link{nettable}}) looking similar to the statistical part
#'   of a GRADE table for a network meta-analysis (Puhan et al.,
#'   2014).
#' }
#' 
#' The following methods are implemented to rank treatments:
#' \itemize{
#' \item rankograms (\code{\link{rankogram}}) (Salanti et al., 2011);
#' \item ranking of treatments (\code{\link{netrank}}) based on
#'   P-scores (Rücker & Schwarzer, 2015) or the Surface Under the
#'   Cumulative RAnking curve (SUCRA) (Salanti et al., 2011);
#' \item partial order of treatment rankings (\code{\link{netposet}},
#'   \code{\link{plot.netposet}}) and Hasse diagram
#'   (\code{\link{hasse}}) according to Carlsen & Bruggemann (2014)
#'   and Rücker & Schwarzer (2017).
#' }
#' 
#' Available functions to evaluate network inconsistency:
#' \itemize{
#' \item split direct and indirect evidence (\code{\link{netsplit}})
#'   to check for consistency (Dias et al., 2010; Efthimiou et al.,
#'   2019);
#' \item net heat plot (\code{\link{netheat}}) and design-based
#'   decomposition of Cochran's Q (\code{\link{decomp.design}})
#'   described in Krahn et al. (2013).
#' }
#' 
#' Additional methods and functions:
#' \itemize{
#' \item information on network connectivity
#'   (\code{\link{netconnection}});
#' \item contribution of direct comparisons to network estimates
#'   (\code{\link{netcontrib}}) (Papakonstantinou et al., 2018; Davies
#'   et al., 2022);
#' \item importance of individual studies measured by reduction of
#'   precision if removed from network (\code{\link{netimpact}})
#'   (Rücker et al., 2020b);
#' \item\sQuote{comparison-adjusted} funnel plot
#'   (\code{\link{funnel.netmeta}}) to assess funnel plot asymmetry in
#'   network meta-analysis (Chaimani & Salanti, 2012);
#' \item conduct pairwise meta-analyses for all comparisons with
#'   direct evidence in a network meta-analysis
#'   (\code{\link{netpairwise}});
#' \item results of several network meta-analyses can be combined with
#'   \code{\link{netbind}} to show these results in a forest plot
#'   (\code{\link{forest.netbind}}).
#' \item measures characterizing the flow of evidence between two
#'   treatments (\code{\link{netmeasures}}) described in König et
#'   al. (2013);
#' \item calculate comparison effects of two arbitrary complex
#'   interventions in component network meta-analysis
#'   (\code{\link{netcomparison}});
#' \item calculate effect of arbitrary complex interventions in
#'   component network meta-analysis (\code{\link{netcomplex}}).
#' }
#' 
#' Functions and datasets from \bold{netmeta} are utilised in
#' Schwarzer et al. (2015), Chapter 8 "Network Meta-Analysis",
#' \url{https://link.springer.com/book/10.1007/978-3-319-21416-0}.
#' 
#' Type \code{help(package = "netmeta")} for a listing of all R
#' functions available in \bold{netmeta}.
#' 
#' Type \code{citation("netmeta")} on how to cite \bold{netmeta} in
#' publications.
#' 
#' To report problems and bugs
#' \itemize{
#' \item type \code{bug.report(package = "netmeta")} if you do not use
#'   RStudio,
#' \item send an email to Guido Schwarzer
#'   \email{guido.schwarzer@@uniklinik-freiburg.de} if you use RStudio.
#' }
#' 
#' The development version of \bold{netmeta} is available on GitHub
#' \url{https://github.com/guido-s/netmeta}.
#' 
#' @name netmeta-package
#' 
#' @docType package
#' 
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}, Gerta
#'   Rücker \email{gerta.ruecker@@uniklinik-freiburg.de}
#' 
#' @references
#' Balduzzi S, Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti
#' G, Efthimiou O, Schwarzer G (2023):
#' netmeta: An R Package for network meta-analysis using frequentist
#' methods.
#' \emph{Journal of Statistical Software},
#' \bold{106}, 1--40
#' 
#' Carlsen L, Bruggemann R (2014):
#' Partial order methodology: a valuable tool in chemometrics.
#' \emph{Journal of Chemometrics},
#' \bold{28}, 226--34
#' 
#' Chaimani A & Salanti G (2012):
#' Using network meta-analysis to evaluate the existence of
#' small-study effects in a network of interventions.
#' \emph{Research Synthesis Methods},
#' \bold{3}, 161--76
#' 
#' Davies AL, Papakonstantinou T, Nikolakopoulou A, Rücker G, Galla T
#' (2022):
#' Network meta-analysis and random walks.
#' \emph{Statistics in Medicine},
#' \bold{41}, 2091--2114
#' 
#' 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
#' 
#' Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G
#' (2019):
#' A Mantel-Haenszel model for network meta-analysis of rare events.
#' \emph{Statistics in Medicine},
#' \bold{38}, 2992--3012
#' 
#' 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
#' 
#' Papakonstantinou, T., Nikolakopoulou, A., Rücker, G., Chaimani, A.,
#' Schwarzer, G., Egger, M., Salanti, G. (2018):
#' Estimating the contribution of studies in network meta-analysis:
#' paths, flows and streams.
#' \emph{F1000Research}
#' 
#' Puhan MA, Schünemann HJ, Murad MH, et al. (2014):
#' A GRADE working group approach for rating the quality of treatment
#' effect estimates from network meta-analysis.
#' \emph{British Medical Journal},
#' \bold{349}, g5630
#' 
#' Rücker G (2012):
#' Network meta-analysis, electrical networks and graph theory.
#' \emph{Research Synthesis Methods},
#' \bold{3}, 312--24
#' 
#' Rücker G, Schwarzer G (2014):
#' Reduce dimension or reduce weights? Comparing two approaches to
#' multi-arm studies in network meta-analysis.
#' \emph{Statistics in Medicine},
#' \bold{33}, 4353--69
#' 
#' Rücker G, Schwarzer G (2015):
#' Ranking treatments in frequentist network meta-analysis works
#' without resampling methods.
#' \emph{BMC Medical Research Methodology},
#' \bold{15}, 58
#' 
#' Rücker G, Schwarzer G (2016):
#' Automated drawing of network plots in network meta-analysis.
#' \emph{Research Synthesis Methods},
#' \bold{7}, 94--107
#' 
#' Rücker G, Schwarzer G (2017):
#' Resolve conflicting rankings of outcomes in network meta-analysis:
#' Partial ordering of treatments.
#' \emph{Research Synthesis Methods},
#' \bold{8}, 526--36
#' 
#' Rücker G, Petropoulou M, Schwarzer G (2020a):
#' Network meta-analysis of multicomponent interventions.
#' \emph{Biometrical Journal},
#' \bold{62}, 808--21
#' 
#' Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Riley
#' RD, Schwarzer G (2020b):
#' The statistical importance of a study for a network meta-analysis
#' estimate.
#' \emph{BMC Medical Research Methodology},
#' \bold{20}, 190
#' 
#' Salanti G, Ades AE, Ioannidis JP (2011):
#' Graphical methods and numerical summaries for presenting results
#' from multiple-treatment meta-analysis: an overview and tutorial.
#' \emph{Journal of Clinical Epidemiology},
#' \bold{64}, 163--71
#' 
#' Schwarzer G, Carpenter JR and Rücker G (2015):
#' \emph{Meta-Analysis with R (Use R!)}.
#' Springer International Publishing, Switzerland.
#'
#' @keywords package
#' 
#' @importFrom meta baujat forest funnel radial trimfill longarm
#'   metabias metabin metacont metagen metainc metacum metainf metareg
#'   gs ci cilayout
#'
#' @importFrom magic adiag
#'
#' @importFrom grDevices colours col2rgb heat.colors rgb xy.coords
#'
#' @importFrom graphics axis box lines par points plot polygon rect
#'   text strheight strwidth title
#'
#' @importFrom stats as.formula dist hclust optim optimize pchisq
#'   prcomp relevel reshape rnorm sd
#'
#' @importFrom utils installed.packages packageDescription
#'   packageVersion
#'
#' @importFrom MASS ginv
#'
#' @importFrom ggplot2 ggplot aes theme_classic geom_tile xlab ylab
#'   theme element_blank element_text scale_fill_gradient2 geom_text
#'   ggtitle scale_x_discrete scale_y_discrete theme_dark
#'
#' @importFrom metafor bldiag contrmat rma.mv


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