R/Franchini2012.R

In guido-s/netmeta: Network Meta-Analysis using Frequentist Methods

#' Network meta-analysis of treatments for Parkinson's disease
#' 
#' @description
#' Network meta-analysis comparing the effects of a number of
#' treatments for Parkinson's disease.
#' 
#' The data are the mean lost work-time reduction in patients given
#' dopamine agonists as adjunct therapy in Parkinson’s disease
#' (Franchini et al. 2012). The data are given as sample size, mean
#' and standard deviation in each trial arm. Treatments are placebo
#' and four active drugs. These data are used as an example in the
#' supplemental material of Dias et al. (2013) where placebo is coded
#' as 1 and the four active drugs as 2 to 5.
#' 
#' @name Franchini2012
#' @aliases parkinson
#' 
#' @docType data
#' 
#' @format A data frame with the following columns:
#' \tabular{rl}{
#' \bold{\emph{Study}}\tab study label \cr
#' \bold{\emph{Treatment1}}\tab treatment 1 \cr
#' \bold{\emph{y1}}\tab treatment effect arm 1 \cr
#' \bold{\emph{sd1}}\tab Standard deviation arm 1 \cr
#' \bold{\emph{n1}}\tab Sample size arm 1 \cr
#' \bold{\emph{Treatment2}}\tab treatment 2 \cr
#' \bold{\emph{y2}}\tab treatment effect arm 2 \cr
#' \bold{\emph{sd2}}\tab Standard deviation arm 2 \cr
#' \bold{\emph{n2}}\tab Sample size arm 2 \cr
#' \bold{\emph{Treatment3}}\tab treatment 3 \cr
#' \bold{\emph{y3}}\tab treatment effect arm 3 \cr
#' \bold{\emph{sd3}}\tab Standard deviation arm 3 \cr
#' \bold{\emph{n3}}\tab Sample size arm 3
#' }
#' 
#' @seealso \code{\link{pairwise}}, \code{\link{metacont}},
#'   \code{\link{netmeta}}, \code{\link{netgraph.netmeta}}
#' 
#' @source
#' Dias S, Sutton AJ, Ades AE and Welton NJ (2013):
#' Evidence synthesis for decision making 2: A generalized linear
#' modeling framework for pairwise and network meta-analysis of
#' randomized controlled trials.
#' \emph{Medical Decision Making},
#' \bold{33}, 607--17
#'
#' Franchini AJ, Dias S, Ades AE, Jansen JP, Welton NJ (2012):
#' Accounting for correlation in network meta-analysis with multi-arm
#' trials.
#' \emph{Research Synthesis Methods},
#' \bold{3}, 142--60
#' 
#' 
#' @keywords datasets
#' 
#' @examples
#' data(Franchini2012)
#' 
#' # Transform data from arm-based format to contrast-based format
#' #
#' p1 <- pairwise(list(Treatment1, Treatment2, Treatment3),
#'   n = list(n1, n2, n3),
#'   mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
#'   data = Franchini2012, studlab = Study)
#' p1
#' 
#' # Conduct network meta-analysis
#' net1 <- netmeta(p1)
#' net1
#' 
#' # Draw network graphs
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#'   thickness = "se.common")
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#'   thickness = "se.common",
#'   iterate = TRUE, plastic = TRUE)
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#'   thickness = "se.common",
#'   iterate = TRUE, plastic = TRUE, start = "eigen")


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