Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5,
warning = FALSE,
eval=rmarkdown::pandoc_available("1.12.3")
)
library(MBNMAtime)
library(rmarkdown)
library(knitr)
library(dplyr)
#load(system.file("extdata", "vignettedata.rda", package="MBNMAtime", mustWork = TRUE))
## ---- results="hide", message=FALSE, eval=FALSE-------------------------------
# # Run an Emax time-course MBNMA using the osteoarthritis dataset
# mbnma <- mb.run(network.pain,
# fun=temax(pool.emax="rel", method.emax="common",
# pool.et50="abs", method.et50="common"),
# rho="dunif(0,1)", covar="varadj")
## ---- results="hide", message=FALSE, echo=FALSE-------------------------------
# Run an Emax time-course MBNMA using the osteoarthritis dataset
network.pain <- mb.network(osteopain)
mbnma <- mb.run(network.pain,
fun=temax(pool.emax="rel", method.emax="common",
pool.et50="abs", method.et50="common"),
rho="dunif(0,1)", covar="varadj", n.iter=3000)
## ---- results="hide", message=FALSE, eval=rmarkdown::pandoc_available("1.12.3")----
# Specify placebo time-course parameters
ref.params <- list(emax=-2)
# Predict responses for a selection of treatments using a stochastic E0 and
# placebo parameters defined in ref.params to estimate the network reference treatment effect
pred <- predict(mbnma, treats=c("Pl_0", "Ce_200", "Du_90", "Et_60",
"Lu_400", "Na_1000", "Ox_44", "Ro_25",
"Tr_300", "Va_20"),
E0=~rnorm(n, 8, 0.5), ref.resp=ref.params)
print(pred)
## ---- results="hide", message=FALSE, eval=rmarkdown::pandoc_available("1.12.3")----
# Generate a dataset of network reference treatment responses over time
placebo.df <- network.pain$data.ab[network.pain$data.ab$treatment==1,]
# Predict responses for a selection of treatments using a deterministic E0 and
#placebo.df to model the network reference treatment effect
pred <- predict(mbnma, treats=c("Pl_0", "Ce_200", "Du_90", "Et_60",
"Lu_400", "Na_1000", "Ox_44", "Ro_25",
"Tr_300", "Va_20"),
E0=10, ref.resp=placebo.df)
print(pred)
## ---- message=FALSE, eval=rmarkdown::pandoc_available("1.12.3")---------------
plot(pred, overlay.ref=TRUE, disp.obs=TRUE)
## ---- fig.height=3, results="hide", eval=FALSE--------------------------------
# # Fit a quadratic time-course MBNMA to the Obesity dataset
# network.obese <- mb.network(obesityBW_CFB, reference = "plac")
#
# mbnma <- mb.run(network.obese,
# fun=tpoly(degree=2,
# pool.1 = "rel", method.1="common",
# pool.2="rel", method.2="common"))
#
# # Define stochastic values centred at zero for network reference treatment
# ref.params <- list(beta.1=~rnorm(n, 0, 0.05), beta.2=~rnorm(n, 0, 0.0001))
#
# # Predict responses within the range of the data
# pred.obese <- predict(mbnma, times=c(0:50), E0=100, treats = c(1,4,15),
# ref.resp=ref.params)
#
# # Plot predictions
# plot(pred.obese, disp.obs = TRUE)
## ---- fig.height=3, results="hide", echo=FALSE, message=FALSE-----------------
# Fit a quadratic time-course MBNMA to the Obesity dataset
network.obese <- mb.network(obesityBW_CFB, reference = "plac")
mbnma <- mb.run(network.obese,
fun=tpoly(degree=2,
pool.1 = "rel", method.1="common",
pool.2="rel", method.2="common"), n.iter=3000)
# Define stochastic values centred at zero for network reference treatment
ref.params <- list(beta.1=~rnorm(n, 0, 0.05), beta.2=~rnorm(n, 0, 0.0001))
# Predict responses within the range of the data
pred.obese <- predict(mbnma, times=c(0:50), E0=100, treats = c(1,4,15),
ref.resp=ref.params)
# Plot predictions
plot(pred.obese, disp.obs = TRUE)
## ---- results="hide", warning=FALSE-------------------------------------------
# Overlay predictions from lumped NMAs between 5-8 and between 8-15 weeks follow-up
plot(pred, overlay.nma=c(5,8,15), n.iter=20000)
## -----------------------------------------------------------------------------
# Predict responses within the range of data
pred.obese <- predict(mbnma, times=c(0:50),
E0=0, ref.resp = NULL)
# Rank predictions at 50 weeks follow-up
ranks <- rank(pred.obese, time=50)
summary(ranks)
plot(ranks)
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