get.relative: Calculates relative effects/mean differences at a particular...

In MBNMAtime: Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

Calculates relative effects/mean differences at a particular time-point

Description

Uses mbnma time-course parameter estimates to calculate treatment differences between treatments or classes at a particular time-point. Can be used to compare treatments evaluated in studies at different follow-up times, or even to compare treatments in different MBNMA models via a common comparator.

Usage

get.relative(
  mbnma,
  mbnma.add = NULL,
  time = max(mbnma$model.arg$jagsdata$time, na.rm = TRUE),
  treats = unique(c(mbnma$network$treatments, mbnma.add$network$treatments)),
  classes = NULL,
  lim = "cred"
)

Arguments

mbnma	An S3 object of class "mbnma" generated by running a time-course MBNMA model
mbnma.add	An S3 object of class("mbnma") generated by running a time-course MBNMA model. This should only be specified if results from two different MBNMA models are to be combined to perform a 2-stage MBNMA (see Details).
time	A numeric value for the time at which to estimate relative effects/mean differences.
treats	A character vector of treatment names for which to calculate relative effects/mean differences. Must be a subset of mbnma$network$treatments.
classes	A character vector of class names for which to calculate relative effects/mean differences. Must be a subset of mbnma$network$classes. Only works for class effect models.
lim	Specifies calculation of either 95% credible intervals (lim="cred") or 95% prediction intervals (lim="pred").

Details

get.relative() can also be used to perform a 2-stage MBNMA that allows synthesis of results from two different MBNMA models via a single common comparator. In an MBNMA model, all treatments must share the same time-course function. However, a 2-stage approach can enable fitting of different time-course functions to different sets ("subnetworks") of treatments. For example, some treatments may have rich time-course information, allowing for a more complex time-course function to be used, whereas others may be sparse, requiring a simpler time-course function.

Relative comparisons between treatments in the two datasets at specific follow-up times can then be estimated from MBNMA predicted effects versus a common comparator using the Bucher method and assuming consistency. See the MBNMAtime vignette for further details.

Value

An object of class "relative.array" list containing:

• The time-point for which results are estimated

• Matrices of posterior means, medians, SDs and upper and lower 95% credible intervals for the differences between each treatment

• An array containing MCMC results for the differences between all treatments specified in treats or all classes specified in classes.

Results are reported in tables as the row-defined treatment minus the column-defined treatment.

Examples

# Create an mb.network object from a dataset
alognet <- mb.network(alog_pcfb)

# Run a quadratic time-course MBNMA using the alogliptin dataset
mbnma <- mb.run(alognet,
  fun=tpoly(degree=2,
  pool.1="rel", method.1="random",
  pool.2="rel", method.2="common"
  )
)

# Calculate differences between all treatments at 20 weeks follow-up
allres <- get.relative(mbnma, time=20)

# Calculate difference between a subset of treatments at 10 weeks follow-up
subres <- get.relative(mbnma, time=10,
  treats=c("alog_50", "alog_25", "placebo"))



###########################
##### 2-stage MBNMA #####
###########################

# Using the osteoarthritis dataset
# With placebo (Pl_0) as common comparator between subnetworks

#### Sparse model ####

# Treatments on which time-course data is limited
sparse.trt <- c("Ce_100", "Ce_400", "Du_90", "Lu_200", "Lu_400",
  "Lu_NA", "Et_5", "Ox_44")

# Create a subnetwork of studies comparing these treatments
sparse.df <- osteopain %>% dplyr::group_by(studyID) %>%
  dplyr::filter(any(treatment %in% sparse.trt)) %>%
  dplyr::ungroup() %>%
  subset(treatment %in% c("Pl_0", sparse.trt))

sparse.net <- mb.network(sparse.df)

# Run a ITP MBNMA with a known rate
sparse.mbnma <- mb.run(sparse.net, fun=titp(method.rate=0.8, pool.rate="abs"))


#### Complex model ####

# Treatments on which time-course data is rich
rich.trt <- levels(osteopain$treatment)[!levels(osteopain$treatment) %in%
  c("Pl_0", "Ce_100", "Ce_400", "Du_90", "Lu_200",
    "Lu_400", "Lu_NA", "Et_5", "Ox_44")]

# Create a subnetwork of studies comparing these treatments
rich.df <- osteopain %>% dplyr::group_by(studyID) %>%
  dplyr::filter(any(treatment %in% rich.trt)) %>%
  dplyr::ungroup() %>%
  subset(treatment %in% c("Pl_0", rich.trt))

rich.net <- mb.network(rich.df)

# Run a Emax MBNMA
rich.mbnma <- mb.run(rich.net, temax(p.expon = FALSE))


#### Calculate relative effects between models ####

# At 10 weeks follow-up
rels.sparse <- get.relative(sparse.mbnma, time=10)
rels.rich <- get.relative(rich.mbnma, time=10)

rels.all <- get.relative(mbnma=rich.mbnma,
  mbnma.add=sparse.mbnma, time=10)

print(rels.all$median)

MBNMAtime documentation built on Oct. 14, 2023, 5:08 p.m.