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Barplot for the Kullback-Leibler divergence measure (missingness scenarios)

Source: R/KLD.barplot_function.R
kld_barplot.Rd

Produces a barplot with the Kullback-Leibler divergence measure from each re-analysis to the primary analysis for a pairwise comparison. Currently, kld_barplot is used concerning the impact of missing participant outcome data.

Usage

kld_barplot(robust, compar, drug_names)

Arguments

robust

An object of S3 class robustness_index. See 'Value' in robustness_index.

compar

A character vector with two elements that indicates the pairwise comparison of interest. The first element refers to the 'experimental' intervention and the second element refers 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. If drug_names is not defined, the order of the interventions as they appear in data is used, instead.

Value

kld_barplot returns a panel of barplots on the Kullback-Leibler divergence measure for each re-analysis.

Details

kld_barplot uses the scenarios inherited by robustness_index via the run_sensitivity function. The scenarios for the missingness parameter (see 'Details' in run_sensitivity) in the compared interventions are split to Extreme, Sceptical, and Optimistic following the classification of Spineli et al. (2021). In each class, bars will green, orange, and red colour refer to scenarios without distance, less distant, and more distant from the primary analysis (the missing-at-random assumption).

kld_barplot 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

Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat 1951;22(1):79–86. doi: 10.1214/aoms/1177729694

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):475–90. doi: 10.1002/jrsm.1478

See also

robustness_index, run_model, run_sensitivity

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'))

# Calculate the robustness index
robust <- robustness_index(sens = res_sens,
                           threshold = 0.17)
#> The value 0.17 was assigned as 'threshold' for standardised mean difference.

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

# Create the barplot for the comparison 'inositol versus placebo'
kld_barplot(robust = robust,
            compar = c("inositol", "placebo"),
            drug_names = interv_names)