#' Calculate the Surface Under the Cumulative Ranking score of from a network meta-analysis #' #' This function calculates the SUCRA (Surface Under the Cumulative Ranking) score from a rank #' probability matrix or an object of class \code{mtc.rank.probability} generated by the \code{\link[gemtc]{rank.probability}} #' function. #' #' @usage sucra(x, lower.is.better = FALSE) #' #' @param x An object of class \code{mtc.rank.probability} generated by the \code{\link[gemtc]{rank.probability}} function #' or a matrix/data.frame in which the rows correspond to the treatment, and columns to the #' probability of a specific treatment having this rank (see Details). Rownames of the matrix should #' contain the name of the specific treatment. #' @param lower.is.better Logical. Do lower (i.e., more negative) effect sizes mean that effects are #' higher? \code{FALSE} by default. Use the default when the provided matrix already contains the #' correct rank probability for each treatment, and values ought not to be inverted. #' #' @details The SUCRA score is a metric to evaluate which treatment in a network is likely #' to be the most efficacious in the context of network meta-analyses. The SUCRA score is calculated #' in the function using the formula described in Salanti, Ades and Ioannidis (2011): #' \deqn{SUCRA_j = \frac{\sum_{b=1}^{a-1}cum_{jb}}{a-1}} #' Where \eqn{j} is some treatment, \eqn{a} are all competing treatments, \eqn{b} are the #' \eqn{b = 1, 2, ..., a-1} best treatments, and \eqn{cum} represents the cumulative probability #' of a treatment being among the \eqn{b} best treatments. #' #' Other than an object of class \code{mtc.rank.probability} for argument \code{x}, the function can also be provided #' with a \eqn{m \times n} matrix where \eqn{m} are rows corresponding to each treatment in the #' network meta-analysis, and the \eqn{n} columns correspond to each rank (1st, 2nd, etc.). Rank probabilities #' should be provided as a value from 0 to 1. Rownames of the matrix should correspond to the treatment names. #' Here is an example rank probability matrix for eight treatments: #' #' \tabular{lrrrrrrrr}{ #' . \tab [,1] \tab [,2] \tab [,3] \tab [,4] \tab [,5] \tab [,6] \tab [,7] \tab [,8]\cr #' CBT \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.001275 \tab 0.087400 \tab 0.911325\cr #' IPT \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.179400 \tab 0.745875 \tab 0.074725\cr #' PDT \tab 0.000000 \tab 0.000000 \tab 0.000225 \tab 0.020300 \tab 0.978025 \tab 0.001450 \tab 0.000000 \tab 0.000000\cr #' PLA \tab 0.002825 \tab 0.551175 \tab 0.262525 \tab 0.181550 \tab 0.001925 \tab 0.000000 \tab 0.000000 \tab 0.000000\cr #' PST \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000025 \tab 0.001450 \tab 0.817850 \tab 0.166725 \tab 0.013950\cr #' SUP \tab 0.000000 \tab 0.216450 \tab 0.398700 \tab 0.383950 \tab 0.000900 \tab 0.000000 \tab 0.000000 \tab 0.000000\cr #' TAU \tab 0.000375 \tab 0.229200 \tab 0.338525 \tab 0.414175 \tab 0.017700 \tab 0.000025 \tab 0.000000 \tab 0.000000\cr #' WLC \tab 0.996800 \tab 0.003175 \tab 0.000025 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 \tab 0.000000 #' } #' #' @references Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019). #' \emph{Doing Meta-Analysis in R: A Hands-on Guide}. DOI: 10.5281/zenodo.2551803. #' \href{https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/bayesian-network-meta-analysis.html}{Chapter 11.2}. #' #'Salanti, G., Ades, A. E. & Ioannidis, J.P.A. (2011). Graphical Methods and Numerical Summaries for #'Presenting Results from Multiple-Treatment Meta-Analysis: An Overview and Tutorial. #'\emph{Journal of Clinical Epidemiology, 64} (2): 163–71. #' #' @author Mathias Harrer & David Daniel Ebert #' #' @import ggplot2 #' #' @export sucra #' #' @seealso #' \code{\link{direct.evidence.plot}} #' #' @examples #' \dontrun{ #' # Example1 : conduct NMA using gemtc, calculate SUCRAs #' suppressPackageStartupMessages(library(gemtc)) #' suppressPackageStartupMessages(library(igraph)) #' data("NetDataGemtc") #' #' network = suppressWarnings(mtc.network(data.re = NetDataGemtc)) #' #' plot(network, layout = layout.fruchterman.reingold) #' #' model = mtc.model(network, linearModel = "fixed", #' n.chain = 4, #' likelihood = "normal", #' link = "identity") #' #' mcmc = mtc.run(model, n.adapt = 5000, n.iter = 100000, thin = 10) #' #' rp = rank.probability(mcmc) #' #' sucra = sucra(rp, lower.is.better = TRUE) #' sucra #' plot(sucra)} #' #' #' #' # Example 2: construct rank proabability matrix, then use sucra function #' rp = rbind(CBT = c(0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.001500, 0.088025, 0.910475), #' IPT = c(0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.176975, 0.748300, 0.074725), #' PDT = c(0.000000, 0.000000, 0.000250, 0.021725, 0.976525, 0.001500, 0.000000, 0.000000), #' PLA = c(0.003350, 0.546075, 0.266125, 0.182125, 0.002325, 0.000000, 0.000000, 0.000000), #' PST = c(0.000000, 0.000000, 0.000000, 0.000000, 0.001500, 0.820025, 0.163675, 0.014800), #' SUP = c(0.000000, 0.217450, 0.403950, 0.378000, 0.000600, 0.000000, 0.000000, 0.000000), #' TAU = c(0.000225, 0.232900, 0.329675, 0.418150, 0.019050, 0.000000, 0.000000, 0.000000), #' WLC = c(0.996425, 0.003575, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)) #' #' sucra(rp, lower.is.better = TRUE) #' plot(sucra(rp, lower.is.better = TRUE)) sucra = function(x, lower.is.better = FALSE) { rank.probability = x # Convert rank.probability to matrix mat = as.matrix(rank.probability) # Loop over treatments, for each treatment: calculate SUCRA a = ncol(mat) j = nrow(mat) names = rownames(mat) sucra = numeric() for (x in 1:j) { sucra[x] = sum(cumsum(mat[x, 1:(a - 1)]))/(a - 1) } # If condition for lower.is.better if (lower.is.better == TRUE) { sucra = numeric() for (x in 1:j) { sucra[x] = 1 - sum(cumsum(mat[x, 1:(a - 1)]))/(a - 1) } } # Make data.frame res = data.frame(Treatment = names, SUCRA = sucra) # Order res = res[order(-res$SUCRA), ] rownames(res) = 1:j rownames(res) = res$Treatment res\$Treatment = NULL class(res) = "sucra" invisible(res) res }