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))
## ---- eval=FALSE--------------------------------------------------------------
# tspline(type="bs", knots=3)
# # ...is equivalent to
# tspline(type="bs", knots=c(0.25,0.5,0.75))
## ---- results="hide"----------------------------------------------------------
# Prepare data using the alogliptin dataset
network.alog <- mb.network(alog_pcfb, reference = "placebo")
# Run a linear time-course MBNMA
mbnma <- mb.run(network.alog, fun=tpoly(degree=1, pool.1="rel", method.1="common"))
## -----------------------------------------------------------------------------
summary(mbnma)
## ---- results="hide"----------------------------------------------------------
# Run an Emax time-course MBNMA with two parameters
mbnma <- mb.run(network.alog, fun=temax(
pool.emax = "rel", method.emax="common",
pool.et50 = "abs", method.et50="common"
))
## -----------------------------------------------------------------------------
summary(mbnma)
## ---- eval=TRUE, results="hide"-----------------------------------------------
# Using the COPD dataset
network.copd <- mb.network(copd)
# Run an log-linear time-course MBNMA
# that accounts for correlation between time points using variance adjustment
mbnma <- mb.run(network.copd,
fun=tloglin(pool.rate="rel", method.rate="random"),
rho="dunif(0,1)", covar="varadj")
## ---- results="hide", message=FALSE, warning=FALSE----------------------------
# Create network object of gout dataset
network.gout <- mb.network(goutSUA_CFBcomb)
# Run a B-spline time-course MBNMA with a knot at 0.2 times the max follow-up
# Common class effect on beta.2, the 2nd spline coefficient
mbnma <- mb.run(network.gout,
fun=tspline(type="bs", knots=c(0.2),
pool.1 = "rel", method.1="common",
pool.2="rel", method.2="random"),
class.effect = list(beta.2="common"))
## -----------------------------------------------------------------------------
summary(mbnma)
## ---- eval=FALSE--------------------------------------------------------------
# mbnma <- mb.run(network.copd,
# fun=tloglin(pool.rate="rel", method.rate="random"),
# priors=list(rate="dnorm(0,2) T(0,)"))
## ---- results="hide"----------------------------------------------------------
# Define informative priors for spline parameters
spline.priors <- list(
d.3 = c(
Aclidinium="dnorm(-0.5, 100)",
Tiotropium="dnorm(0, 0.0001)"
),
d.4 = c(
Aclidinium="dnorm(0, 100)",
Tiotropium="dnorm(0, 0.0001)"
))
# Using the COPD dataset with a B-spline MBNMA
mbnma <- mb.run(network.copd, fun=tspline(degree=2, knots=c(0.1,0.5)),
priors=spline.priors)
## -----------------------------------------------------------------------------
# Predict and plot time-course relative effect
pred <- predict(mbnma)
plot(pred)
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