data_preparation {rnmamod}

Prepare the dataset in the proper format for R2jags

Description

data_preparation prepares the dataset in the proper format for R2jags and returns a list of elements that run_model inherits via the argument data.

Usage

data_preparation(data, measure)

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.

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.

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.

Author(s)

Loukia M. Spineli

See Also

heatmap_missing_dataset, heatmap_missing_network, R2jags, run_metareg, run_model, run_nodesplit, run_sensitivity, run_ume, prepare_model, prepare_nodesplit, prepare_ume