Dendrogram with amalgamated heatmap (Comparisons' comparability for transitivity evaluation)

dendro_heatmap creates a dendrogram alongside the heatmap of Gower dissimilarities among the trials in the network for a specific linkage method and number of clusters (Spineli et al., 2025).

Usage

dendro_heatmap(input, label_size = 12, axis_text_size = 10)

Arguments

input: An object of S3 class comp_clustering. See 'Value' in comp_clustering.
label_size: A positive integer for the font size of the heatmap elements. label_size determines the size argument found in the geom's aesthetic properties in the R-package ggplot2.
axis_text_size: A positive integer for the font size of row and column names of the heatmap. axis_text_size determines the axis.text argument found in the theme's properties in the R-package ggplot2.

Value

dendro_heatmap uses the heatmaply function of the R-package heatmaply to create a cluster heatmap for a selected linkage method and number of clusters. The function uses different colours to indicate the clusters directly on the dendrogram, specified using the R-package dendextend. The names of the leaves refer to the trials and corresponding pairwise comparison.

@details The function inherits the linkage method and number of optimal clusters by the comp_clustering function.

Remember: when using the comp_clustering function, inspect the average silhouette width for a wide range of clusters to decide on the optimal number of clusters.

References

Spineli LM, Papadimitropoulou K, Kalyvas C. Exploring the Transitivity Assumption in Network Meta-Analysis: A Novel Approach and Its Implications. Stat Med 2025;44(7):e70068. doi: 10.1002/sim.70068.

Author

Loukia M. Spineli

Examples

# \donttest{
# Fictional dataset
data_set <- data.frame(Trial_name = 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")))
#> Warning: NAs introduced by coercion

# Apply hierarchical clustering (informative = FALSE)
hier <- comp_clustering(input = data_set,
                        drug_names = c("A", "B", "C"),
                        threshold = 0.13,  # General research setting
                        informative = FALSE,
                        optimal_clusters = 3,
                        get_plots = TRUE)
#> - 3 observed comparisons (0 single-study comparisons)
#> - Dropped characteristics: blinding
#> Warning: data length [63] is not a sub-multiple or multiple of the number of columns [2]
#> - Cophenetic coefficient: 0.911
#> - Optimal linkage method: average
#> $Trials_diss_table
#>       1 B-A 2 B-A 3 B-A 4 C-A 5 C-A 6 C-B 7 C-B
#> 1 B-A 0.000    NA    NA    NA    NA    NA    NA
#> 2 B-A 0.023 0.000    NA    NA    NA    NA    NA
#> 3 B-A 0.045 0.023 0.000    NA    NA    NA    NA
#> 4 C-A 0.955 0.977 1.000 0.000    NA    NA    NA
#> 5 C-A 0.932 0.955 0.977 0.023 0.000    NA    NA
#> 6 C-B 0.579 0.602 0.624 0.376 0.353 0.000    NA
#> 7 C-B 0.556 0.579 0.602 0.398 0.376 0.023     0
#> 
#> $Comparisons_diss_table
#>      B-A  C-A  C-B
#> B-A 0.03   NA   NA
#> C-A 0.97 0.02   NA
#> C-B 0.59 0.38 0.02
#> 
#> $Total_dissimilarity
#>   comparison total_dissimilarity         index_type
#> 5 C-A vs C-B                0.38 Between-comparison
#> 3 B-A vs C-B                0.59 Between-comparison
#> 2 B-A vs C-A                0.97 Between-comparison
#> 4        C-A                0.02  Within-comparison
#> 6        C-B                0.02  Within-comparison
#> 1        B-A                0.03  Within-comparison
#> 
#> $Types_used
#>   characteristic    type
#> 1         sample  double
#> 2            age  double
#> 3       blinding integer
#> 
#> $Total_missing
#> [1] "33.33%"
#> 
#> $Table_average_silhouette_width
#>   clusters silhouette
#> 1        2      0.782
#> 2        3      0.943
#> 3        4      0.608
#> 4        5      0.339
#> 5        6      0.071
#> 
#> $Table_cophenetic_coefficient
#>   linkage_methods results
#> 5         average   0.911
#> 6        mcquitty   0.911
#> 7          median   0.910
#> 8        centroid   0.909
#> 4        complete   0.908
#> 2         ward.D2   0.907
#> 1          ward.D   0.902
#> 3          single   0.901
#> 
#> $Optimal_link
#> [1] "average"
#> 
#> $Cluster_comp
#>   study cluster sil_width
#> 1     1       1 0.9399199
#> 2     2       1 0.9614891
#> 3     3       1 0.9443758
#> 4     4       2 0.9412916
#> 5     5       2 0.9376299
#> 6     6       3 0.9376299
#> 7     7       3 0.9412916
#> 
#> $Within_comparison_dissimilarity

#> 
#> $Between_comparison_dissimilarity

#> 
#> $Profile_plot

#> 
#> $Silhouette_width_plot

#> 
#> $Barplot_comparisons_cluster

#> 
#> attr(,"class")
#> [1] "comp_clustering"

# Create the dendrogram with integrated heatmap
dendro_heatmap(hier)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the dendextend package.
#>   Please report the issue at <https://github.com/talgalili/dendextend/issues>.

# }