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

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.

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.

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 similarity values appear as dotted, vertical, black lines on each bar.

References

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

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 = factor(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)
#> - 3 observed comparisons (0 single-study comparisons)
#> - Dropped characteristics: none
#> $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 0.015 0.000    NA    NA    NA    NA    NA
#> 3 B-A 0.030 0.015 0.000    NA    NA    NA    NA
#> 4 C-A 0.970 0.985 1.000 0.000    NA    NA    NA
#> 5 C-A 0.955 0.970 0.985 0.015 0.000    NA    NA
#> 6 C-B 0.719 0.734 0.749 0.251 0.235 0.000    NA
#> 7 C-B 0.704 0.719 0.734 0.266 0.251 0.015     0
#> 
#> $Comparisons_diss_table
#>      B-A  C-A  C-B
#> B-A 0.02   NA   NA
#> C-A 0.98 0.02   NA
#> C-B 0.73 0.25 0.02
#> 
#> $Total_dissimilarity
#>   comparison total_dissimilarity         index_type
#> 5 C-A vs C-B                0.25 Between-comparison
#> 3 B-A vs C-B                0.73 Between-comparison
#> 2 B-A vs C-A                0.98 Between-comparison
#> 1        B-A                0.02  Within-comparison
#> 4        C-A                0.02  Within-comparison
#> 6        C-B                0.02  Within-comparison
#> 
#> $Types_used
#>   characteristic    type
#> 1         sample  double
#> 2            age  double
#> 3       blinding integer
#> 
#> $Total_missing
#> [1] "0%"
#> 
#> $Within_comparison_dissimilarity

#> 
#> $Between_comparison_dissimilarity

#> 
#> $Dissimilarity_heatmap

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

plot_study_dissimilarities(results = res,
                           axis_title_size = 12,
                           axis_text_size = 12,
                           strip_text_size = 11,
                           label_size = 3.5)
#> [[1]]

#> 
#> $within
#>   study_id comp      value
#> 1        1  B-A 0.02371708
#> 2        2  B-A 0.01500000
#> 3        3  B-A 0.02371708
#> 4        4  C-A 0.01500000
#> 5        5  C-A 0.01500000
#> 6        6  C-B 0.01500000
#> 7        7  C-B 0.01500000
#> 
#> $between
#>   study_id comp     value
#> 1        1  B-A 0.8463897
#> 2        2  B-A 0.8612262
#> 3        3  B-A 0.8760682
#> 4        4  C-A 0.7803694
#> 5        5  C-A 0.7669910
#> 6        6  C-B 0.5890576
#> 7        7  C-B 0.5805325
#> 
# }