rnmamod (version 0.4.0)

rankosucra_plot: Rankograms and SUCRA curves

Description

It returns a panel of rankograms with integrated SUCRA curves for each intervention in the network. The function can illustrate the results of a single or two outcomes simultaneously.

Usage

rankosucra_plot(
  full1,
  full2 = NULL,
  drug_names1,
  drug_names2 = NULL,
  name1 = NULL,
  name2 = NULL
)

Value

A panel of rankograms (yellowish bars) with integrated reddish SUCRA curves for each intervention in the network (Salanti et al., 2011). The x-axis of each panel refers to the ranking, and the y-axis refers to the ranking probability expressed in percentage.

Arguments

full1

An object of S3 class run_model for network meta-analysis. See 'Value' in run_model.

full2

An object of S3 class run_model for network meta-analysis of a second outcome. See 'Value' in run_model.

drug_names1

A vector of labels with the name of the interventions in the order they appear in the argument data of run_model for full1.

drug_names2

A vector of labels with the name of the interventions in the order they appear in the argument data of run_model for full2. The elements must be a subset of drug_names1.

name1

The text for the title of the results that refer to the outcome under full1.

name2

The text for the title of the results that refer to the outcome under full2.

Author

Loukia M. Spineli, Chrysostomos Kalyvas, Katerina Papadimitropoulou

Details

Interventions are sorted in the descending order of their SUCRA value. The SUCRA value expressed in percentage appears on the top left corner of each panel. In the case of two outcomes, the SUCRA values of the outcome under the argument full1 are considered to sort the interventions from the best to the worst.

When a second outcome is also considered, different colours are used to draw the corresponding SUCRA curves and the rankograms: green for the outcome under full1, and red for the outcome under full2.

rankosucra_plot can be used only for a network of interventions. Otherwise, the execution of the function will be stopped and an error message will be printed on the R console.

References

Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011;64(2):163--71. doi: 10.1016/j.jclinepi.2010.03.016

See Also

run_model

Examples

data("nma.liu2013")

# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_liu.rds', package = 'rnmamod'))

# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "pramipexole",
                  "serotonin-norepinephrine reuptake inhibitor",
                  "serotonin reuptake inhibitor",
                  "tricyclic antidepressant", "pergolide")

# Create the integrated rankograms and SUCRA curves
rankosucra_plot(full1 = res,
                drug_names1 = interv_names)