Prepare the dataset in the proper format for R2jags
Source:R/data.preparation_function.R
data_preparation.Rddata_preparation prepares the dataset in the proper format for
R2jags and returns a list of elements that run_model inherits
via the argument data.
Arguments
- data
A data-frame of the one-trial-per-row format with arm-level data. See 'Format' in
run_model.- measure
Character string indicating the effect measure. For a binary outcome, the following can be considered:
"OR","RR"or"RD"for the odds ratio, relative risk, and risk difference, respectively. For a continuous outcome, the following can be considered:"MD","SMD", or"ROM"for mean difference, standardised mean difference and ratio of means, respectively.
Value
A list of data-frames on the following elements to be passed
to run_model:
- pseudo_m
A pseudo-data-frame with values -1 and m for the corresponding trial-arms with unreported and reported missing participant outcome data, respectively (see 'Details').
- m
The number of missing participant outcome data in each trial-arm (see 'Details').
- N
The number of randomised participants in each trial-arm.
- t
The intervention identifier in each trial-arm.
- I
A pseudo-data-frame that indicates whether missing participant outcome data have been reported or not for each observed trial-arm (see 'Details').
- measure
The effect measure for the analysed outcome.
- y0
The observed mean value of the outcome in each trial-arm, when the outcome is continuous.
- se0
The observed standard deviation of the outcome in each trial-arm, when the outcome is continuous.
- r
The number of observed events of the outcome in each trial-arm, when the outcome is binary.
Details
data_preparation prepares the data for the Bayesian analysis
(See 'Format' in run_model). data_preparation
creates the pseudo-data-frames m_new, I, and m_pseudo
that have the same dimensions with the element N. m_new takes
the zero value for the observed trial-arms with unreported missing
participant outcome data (i.e., m equals NA for the
corresponding trial-arms), the same value with m for the observed
trial-arms with reported missing participant outcome data, and NA
for the unobserved trial-arms. I is a dummy data-frame and takes the
value one for the observed trial-arms with reported missing participant
outcome data, the zero value for the observed trial-arms with unreported
missing participant outcome data (i.e., m_new equals zero for the
corresponding trial-arms), and NA for the unobserved trial-arms.
Thus, I indicates whether missing participant outcome data have been
collected for the observed trial-arms. If the user has not defined the
element m in data_preparation, m_new and I
take the zero value for all observed trial-arms to indicate that no missing
participant outcome data have been collected for the analysed outcome.
I and m_new are used from the following functions of the
package: run_model, run_metareg,
prepare_model, run_nodesplit,
prepare_nodesplit, run_ume,
prepare_ume, and run_sensitivity.
Lastly, m_pseudo is a variant of m_new: it takes the value -1
for the observed trial-arms with unreported missing participant outcome
data (i.e., m equals NA for the corresponding trial-arms),
the same value with m for the observed trial-arms with reported
missing participant outcome data, and NA for the unobserved
trial-arms. It is used in function heatmap_missing_network to
calculate and illustrate the percentage of missing participant outcome data
across the observed comparisons and interventions of the network and the
function heatmap_missing_dataset to illustrate the trial-arms
with unreported missing participant outcome data. All pseudo-data-frames
aim to retain the trials without information on missing participant outcome
data.
Furthermore, data_preparation sorts the interventions across
the arms of each trial in an ascending order and correspondingly the
remaining elements in data (See 'Format' in
run_model). data_preparation considers the first
column in t as being the control arm for every trial. Thus,
this sorting ensures that interventions with a lower identifier are
consistently treated as the control arm in each trial. This case is
relevant in non-star-shaped networks.