Converts MBNMA data frame to a list for use in JAGS model
getjagsdata(
data.ab,
fun = NULL,
class = FALSE,
rho = NULL,
covstruct = "CS",
link = "identity",
sdscale = FALSE,
cfb = NULL
)A named list of numbers, vector, matrices and arrays to be sent to JAGS. List elements are:
y An array of mean responses for each observation in each arm within each study
se An array of standard errors for each observation in each arm within each study
time A matrix of follow-up times within each study
fups A numeric vector with the number of follow-up measurements per study
narm A numeric vector with the number of arms per study
NS The total number of studies in the dataset
NT The total number of treatments in the dataset
treat A matrix of treatment codes within each study
Nclass Optional. The total number of classes in the dataset
class Optional. A matrix of class codes within each study
classkey Optional. A vector of class codes that correspond to treatment codes.
Same length as the number of treatment codes.
mat.triangle Optional. A matrix with number indicating how to fill covariance
matrices within the JAGS code.
mat.order Optional. A matrix with number indicating what order to fill
covariance matrices within the JAGS code.
timedif.0 Optional. A vector of the difference in times between the first and second
follow-up time in each study.
A data frame of arm-level data in "long" format containing the columns:
studyID Study identifiers
time Numeric data indicating follow-up times
y Numeric data indicating the aggregate response for a given observation (e.g. mean)
se Numeric data indicating the standard error for a given observation
treatment Treatment identifiers (can be numeric, factor or character)
class An optional column indicating a particular class identifier. Observations with the same treatment
identifier must also have the same class identifier.
n An optional column indicating the number of participants used to calculate the
response at a given observation (required if modelling using Standardised Mean Differences)
standsd An optional column of numeric data indicating reference SDs used to standardise
treatment effects when modelling using Standardised Mean Differences (SMD).
An object of class "timefun" generated (see Details) using any of
tloglin(), tpoly(), titp(), temax(), tfpoly(), tspline() or tuser()
A boolean object indicating whether or not data.ab contains
information on different classes of treatments
The correlation coefficient when modelling within-study correlation between time points. The default is a string representing a
prior distribution in JAGS, indicating that it be estimated from the data (e.g. rho="dunif(0,1)"). rho also be assigned a
numeric value (e.g. rho=0.7), which fixes rho in the model to this value (e.g. for use in a deterministic sensitivity analysis).
If set to rho=0 (the default) then this implies modelling no correlation between time points.
A character to indicate the covariance structure required for modelling correlation between
time points (if any), since
this determines some of the data. Can be either "CS" (compound symmetry), "AR1" (autoregressive AR1) or
"varadj" (variance-adjustment).
Can take either "identity" (the default),
"log" (for modelling Ratios of Means friedrich2011MBNMAtime) or
"smd" (for modelling Standardised Mean Differences - although this also corresponds to an identity link function).
Logical object to indicate whether to write a model that specifies a reference SD
for standardising when modelling using Standardised Mean Differences. Specifying sdscale=TRUE
will therefore only modify the model if link function is set to SMD (link="smd").
A logical vector whose length is equal to the unique number of studies in data.ab, where each
element is TRUE if the study data reported is change-from-baseline and FALSE otherwise. If left as NULL
(the default) then this will be identified from the data by assuming any study for which there is no data
at time=0 reports change-from-baseline.
# Using the alogliptin dataset
network <- mb.network(alog_pcfb)
jagsdat <- getjagsdata(network$data.ab)
# Get JAGS data with class
netclass <- mb.network(goutSUA_CFBcomb)
jagsdat <- getjagsdata(netclass$data.ab, class=TRUE)
# Get JAGS data that allows for modelling correlation between time points
painnet <- mb.network(osteopain)
jagsdat <- getjagsdata(painnet$data.ab, rho="dunif(0,1)", covstruct="AR1")
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