Enhanced balloon plot

Creates the enhanced balloon plot for the summary effect size and between-trial standard deviation, tau, under different scenarios about the missingness parameter for a pair of interventions. balloon_plot uses the scenarios considered in run_sensitivity.

Usage

balloon_plot(sens, compar, drug_names)

Arguments

sens: An object of S3 class run_sensitivity. See 'Value' in run_sensitivity.
compar: A character vector with two elements indicating the pairwise comparison of interest. The first element refers to the 'experimental' and the second element to the 'control' intervention of the comparison.
drug_names: A vector of labels with the name of the interventions in the order they appear in the argument data of run_model.

Value

balloon_plot returns two enhanced balloon plots for one comparison (see 'Details'):

plot_effect_size: The enhanced balloon plot for the summary effect size (according to the argument measure inherited by run_sensitivity) for one pairwise comparison.
plot_tau: The enhanced balloon plot for tau. When the fixed-effect model has been performed in run_sensitivity, the function will not return the plot_tau.

Details

For the plot_effect_size of the selected pairwise comparison, the different colours and sizes of the bubbles reflect the posterior standard deviation and the posterior mean, respectively. A colour key appears below the plot. The size of the bubble is proportional to the corresponding posterior mean. Crossed bubbles indicate scenarios with conclusive evidence (the 95% credible interval excludes the null value), and filled bubbles indicate scenarios with inconclusive evidence (the 95% credible interval includes the null value). The missing-at-random assumption (primary analysis) is labeled in a white frame. Both axes illustrate the scenarios as specified in the argument mean_scenarios of the run_sensitivity: the x-axis refers to the 'experimental' intervention, and the y-axis refers to the 'control' intervention.

The same enhanced balloon plot is created for tau (plot_tau). However, filled bubbles indicate low statistical heterogeneity (the posterior median of tau is lower than the median of the prior distribution for the heterogeneity parameter), and crossed bubbles indicate considerable statistical heterogeneity (the posterior median of tau exceeds the median of the prior distribution for the heterogeneity parameter).

balloon_plot can be used only when missing participant outcome data have been extracted for at least one trial. Otherwise, the execution of the function will be stopped and an error message will be printed on the R console.

References

Spineli LM, Kalyvas C, Papadimitropoulou K. Quantifying the robustness of primary analysis results: A case study on missing outcome data in pairwise and network meta-analysis. Res Synth Methods 2021;12(4):475–490. doi: https://doi.org/10.1002/jrsm.1478

Author

Loukia M. Spineli

Examples

data("pma.taylor2004")

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

# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "inositol")

# Create the enhanced balloon plot for 'inositol versus placebo'
balloon_plot(sens = res_sens,
             compar = c("inositol", "placebo"),
             drug_names = interv_names)
#> $plot_effect_size
#> Warning: All aesthetics have length 1, but the data has 25 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.

#> 
#> $plot_tau
#> Warning: All aesthetics have length 1, but the data has 25 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.

#>