plot_study_dissimilarities: Plot Gower's disimilarity values for each study (Transitivity...

In rnmamod: Bayesian Network Meta-Analysis with Missing Participants

Plot Gower's disimilarity values for each study (Transitivity evaluation)

Description

Illustrating the range of Gower's dissimilarity values for each study in the network, as well as their between- and within-comparison dissimilarities

Usage

plot_study_dissimilarities(
  results,
  axis_title_size = 12,
  axis_text_size = 12,
  strip_text_size = 11,
  label_size = 3.5
)

Arguments

results	An object of S3 class comp_clustering. See 'Value' in comp_clustering.
axis_title_size	A positive integer for the font size of axis title (both axes). axis_title_size determines the axis.title argument found in the theme's properties in the R-package ggplot2.
axis_text_size	A positive integer for the font size of axis text (both axes). axis_text_size determines the axis.text argument found in the theme's properties in the R-package ggplot2.
strip_text_size	A positive integer for the font size of facet labels. strip_text_size determines the strip.text argument found in the theme's properties in the R-package ggplot2.
label_size	A positive integer for the font size of labels appearing on each study-specific segment. label_size determines the size argument found in the geom's aesthetic properties in the R-package ggplot2.

Details

The range of Gower's dissimilarity values for each study versus the remaining studies in the network for a set of clinical and methodological characteristics that may act as effect modifiers. Gower's dissimilarities take values from 0 to 1, with 0 and 1 implying perfect similarity and perfect dissimilarity, respectively.

The unique dissimilarity values appear as dotted, vertical, grey lines on each study

Value

A horizontal bar plot illustrating the range of Gower's dissimilarity values for each study with those found in other comparisons. The study names appear on the y-axis in the order they appear in results and the dissimilarity values appear on the x-axis. Red and blue points refer to the (average) within-comparison and between-comparison dissimilarity, respectively, for each study.

A data-frame on the (average) within-comparison and between-comparison dissimilarities for each study alongside the study name and comparison. The last two columns refer to the within-comparison and between-comparison dissimilarities, respectively, after replacing with the maximum value in the multi-arm trials. These two columns should be used as a covariate in the function study_perc_contrib to obtain the percentage contribution of each study based on the covariate values.

Author(s)

Loukia M. Spineli

References

Gower J. General Coefficient of Similarity and Some of Its Properties. Biometrics 1971;27(4):857–71. doi: 10.2307/2528823

See Also

comp_clustering, study_perc_contrib

Examples

# Fictional dataset
data_set <- data.frame(Trial_name = paste("study", as.character(1:7)),
                      arm1 = c("1", "1", "1", "1", "1", "2", "2"),
                      arm2 = c("2", "2", "2", "3", "3", "3", "3"),
                      sample = c(140, 145, 150, 40, 45, 75, 80),
                      age = c(18, 18, 18, 48, 48, 35, 35),
                      blinding = as.integer(c("yes", "yes", "yes", "no", "no", "no", "no")))

# Obtain comparison dissimilarities (informative = TRUE)
res <- comp_clustering(input = data_set,
                       drug_names = c("A", "B", "C"),
                       threshold = 0.13,  # General research setting
                       informative = TRUE,
                       get_plots = TRUE)

plot_study_dissimilarities(results = res,
                           axis_title_size = 12,
                           axis_text_size = 12,
                           strip_text_size = 11,
                           label_size = 3.5)

rnmamod documentation built on July 13, 2026, 9:06 a.m.