rnmamod (version 0.4.0)

heatmap_robustness: Heatmap of robustness

Description

Facilitates the detection of comparisons that are associated with a lack of robustness in the context of a sensitivity analysis.

Usage

heatmap_robustness(robust, drug_names)

Value

heatmap_robustness first prints on the R console a message on the threshold of robustness determined by the user in

robustness_index and robustness_index_user. Then, it returns a lower triangular heatmap matrix with the robustness index value of all possible pairwise comparisons.

Arguments

robust

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

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.

Author

Loukia M. Spineli

Details

The heatmap illustrates the robustness index for each possible pairwise comparison in the network. The pairwise comparisons are read from left to right. Comparisons highlighted with green or red colour imply robust or frail conclusions for the primary analysis, respectively. This corresponds to robustness index below or at least the selected threshold of robustness. heatmap_robustness inherits the threshold of robustness selected in the robustness_index or robustness_index_user function. The robustness index of each pairwise comparison also appears in the corresponding cell. When there is at least one comparison with frail conclusions, the primary analysis results may be questionable for the whole network (Spineli et al., 2021).

heatmap_robustness is not restricted to the sensitivity analysis concerning the impact of missing participant outcome data.

heatmap_robustness 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

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, robustness_index_user, run_model

Examples

data("nma.baker2009")

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

# Calculate the robustness index
robust <- robustness_index(sens = res_sens,
                           threshold = 0.28)

# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "budesonide", "budesonide plus formoterol",
                  "fluticasone", "fluticasone plus salmeterol",
                  "formoterol", "salmeterol", "tiotropium")

# Create the heatmap of robustness
heatmap_robustness(robust = robust,
                   drug_names = interv_names)