Network Meta-Analysis for Decision-Making

Appendices

Appendix A Sample R Code to Obtain Adjusted Standard Errors Using Netmeta (Chapter 7)

Available as electronic file in AppAnetmetascript.R.

# Full Thrombo example: load data
datanet <- read.csv("DataThromb.csv", header=TRUE, sep=",")
TreatCodes <- read.csv("TreatCodes.csv", header=TRUE, sep=",")
print(TreatCodes)
#
######################################################
#    Obtain reduced weights for FE model
######################################################
library(netmeta)
# Gerta Rücker, Guido Schwarzer, Ulrike Krahn and Jochem König (2016).
# netmeta: Network Meta-Analysis using Frequentist Methods. R package
# version 0.9-1. https://CRAN.R-project.org/package=netmeta
p1 <- pairwise(treat=list(t1, t2, t3), 
              event=list(r1, r2, r3),
              n=list(n1,n2,n3),
              data=datanet, studlab=study)
print(p1)
# net1 <- netmeta(TE, seTE, treat1,treat2,studlab, data=p1)
print(net1)
# study 1
s1 <- net1$studlab == 1   # choose study 1
net1$seTE[s1]            # Unadjusted standard errors
net1$seTE.adj[s1]        # Adjusted standard errors
# study 6
s2 <- net1$studlab == 6   # choose study 6
net1$seTE[s2]   # Unadjusted standard errors (as given in the data)
net1$seTE.adj[s2]        # Adjusted standard errors
#
# DATA FOR WinBUGS: treatment difference and adjusted st. errors
# change sign to be log-OR of treat 2 compared to 1
cbind(t1=c(net1$treat1[s1], net1$treat1[s2]), 
      t2=c(net1$treat2[s1], net1$treat2[s2]),
      y2=c(-net1$TE[s1], -net1$TE[s2]), 
      se2=c(net1$seTE.adj[s1], net1$seTE.adj[s2]),
 study=c(rep(1,3), ...