Nothing
## ----setup, include = FALSE---------------------------------------------------
library(MBNMAdose)
#devtools::load_all()
library(rmarkdown)
library(knitr)
library(dplyr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5,
include=TRUE,
tidy.opts=list(width.cutoff=80),
tidy=TRUE
)
## ---- results="hide", warning=FALSE, message=FALSE----------------------------
# Using the alogliptin dataset
alognet <- mbnma.network(alog_pcfb)
nma <- nma.run(alognet, method="random")
ume <- nma.run(alognet, method="random", UME = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
kable(data.frame(
"Model"=c("NMA", "UME"),
"resdev"=c(nma$jagsresult$BUGSoutput$median$totresdev,
ume$jagsresult$BUGSoutput$median$totresdev),
"sd"=c(MBNMAdose:::neatCrI(nma$jagsresult$BUGSoutput$summary[rownames(nma$jagsresult$BUGSoutput$summary)=="sd", c(3,5,7)], digits = 2),
MBNMAdose:::neatCrI(ume$jagsresult$BUGSoutput$summary[rownames(ume$jagsresult$BUGSoutput$summary)=="sd", c(3,5,7)], digits=2))
), digits=2,
col.names=c("Model", "Residual Deviance", "Betwen-study SD"))
## ---- results="hide", warning=FALSE, message=FALSE----------------------------
# Compares residual deviance contributions from NMA and UME models
devdev(nma, ume, dev.type="resdev")
## ---- results="hide", warning=FALSE, message=FALSE, fig.show = "hide", eval=FALSE----
# # Using the psoriasis dataset (>75% improvement in PASI score)
# psoriasis$r <- psoriasis$r75
# psorinet <- mbnma.network(psoriasis)
#
# # Identify comparisons on which node-splitting is possible
# splitcomps <- inconsistency.loops(psorinet$data.ab, incldr=TRUE)
# print(splitcomps)
#
# # If we want to fit an Emax dose-response function, there is insufficient
# #indirect evidence in all but the first 6 comparisons
# nodesplit <- mbnma.nodesplit(psorinet, fun=demax(), comparisons=splitcomps[1:6,], method="common")
## ---- eval=FALSE--------------------------------------------------------------
# print(nodesplit)
## ---- eval=FALSE--------------------------------------------------------------
# # Plot forest plots of direct, indirect and pooled (MBNMA) results for each comparison
# plot(nodesplit, plot.type="forest")
#
# # Plot posterior densities of direct and indirect results for each nodesplit comparisons
# plot(nodesplit, plot.type="density")
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