# Maraca Plots - Validation

#### Monika Huhn

#### 2025-11-20

This vignette is largely based on the PharmaSUG 2023 conference paper.

When using a maraca plot in a regulatory setting, for example to visualize clinical study results for regulatory submissions, there will be strict validation demands. For example, the results might need to be double programmed for validation purposes.

In order to facilitate the validation of the graphic output, the maraca package includes the function `validate_maraca_plot()` that allows the user to extract important metrics from the plot itself. This allows to programmatically compare the results of a plot produced using the maraca package with other programmatic approaches. The paper linked above walks through a validation example where the double programming was done in SAS.

To use the validation functionality, we need to first create a maraca object.

    library(maraca)

    data(hce_scenario_a)

    maraca_dat <- maraca(
      data = hce_scenario_a,
      step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"),
      last_outcome = "Continuous outcome",
      fixed_followup_days = 3 * 365,
      column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"),
      arm_levels = c(active = "Active", control = "Control"),
      compute_win_odds = TRUE
    )

We then create a maraca plot and save the actual plot as an object.

    # Save plot as its own object
    maraca_plot <- plot(maraca_dat)
    # The plot has its own class called "maracaPlot"
    class(maraca_plot)
    ## [1] "maracaPlot"      "ggplot2::ggplot" "ggplot"          "ggplot2::gg"
    ## [5] "S7_object"       "gg"
    # Display plot
    maraca_plot

Now we can validate the plot using the `validate_maraca_plot()` function.

    validation_list <- validate_maraca_plot(maraca_plot)
    ## Joining with `by = join_by(group)`
    ## Joining with `by = join_by(group)`
    # Display which metrics are included
    str(validation_list)
    ## List of 9
    ##  $ plot_type       : chr "default"
    ##  $ proportions     : Named num [1:5] 13.8 18.1 12.7 9.8 45.6
    ##   ..- attr(*, "names")= chr [1:5] "Outcome I" "Outcome II" "Outcome III" "Outcome IV" ...
    ##  $ tte_data        :'data.frame':    544 obs. of  3 variables:
    ##   ..$ x    : num [1:544] 0.125 0.159 0.463 0.591 0.621 ...
    ##   ..$ y    : num [1:544] 0.2 0.4 0.6 0.2 0.4 0.6 0.8 0.8 1 1.2 ...
    ##   ..$ group: Factor w/ 2 levels "Active","Control": 2 2 2 1 1 1 2 1 2 2 ...
    ##  $ binary_step_data: NULL
    ##  $ binary_last_data: NULL
    ##  $ scatter_data    : NULL
    ##  $ boxstat_data    :'data.frame':    2 obs. of  9 variables:
    ##   ..$ group        : Factor w/ 2 levels "Active","Control": 1 2
    ##   ..$ whisker_lower: num [1:2] 63.8 57.8
    ##   ..$ hinge_lower  : num [1:2] 74.9 71
    ##   ..$ median       : num [1:2] 79.7 75.9
    ##   ..$ hinge_upper  : num [1:2] 83.9 80.7
    ##   ..$ whisker_upper: num [1:2] 95.5 94.5
    ##   ..$ outliers     :List of 2
    ##   .. ..$ : num 60.8
    ##   .. ..$ : num [1:5] 54.4 96.4 97.2 99.7 100
    ##   ..$ x_lowest     : num [1:2] 60.8 54.4
    ##   ..$ x_highest    : num [1:2] 95.5 100
    ##  $ violin_data     :'data.frame':    2060 obs. of  7 variables:
    ##   ..$ group         : Factor w/ 2 levels "Active","Control": 1 1 1 1 1 1 1 1 1 1 ...
    ##   ..$ x             : num [1:2060] 60.8 60.8 60.9 61 61 ...
    ##   ..$ y             : num [1:2060] 44 44 44 44 44 44 44 44 44 44 ...
    ##   ..$ density       : num [1:2060] 1.44 1.48 1.51 1.55 1.6 ...
    ##   ..$ density_scaled: num [1:2060] 0.0197 0.0202 0.0208 0.0213 0.0219 ...
    ##   ..$ violinwidth   : num [1:2060] 44.2 44.2 44.2 44.2 44.2 ...
    ##   ..$ width         : num [1:2060] 18.7 18.7 18.7 18.7 18.7 ...
    ##  $ wo_stats        : Named num [1:4] 1.64 1.42 1.91 9.35e-12
    ##   ..- attr(*, "names")= chr [1:4] "winodds" "lowerCI" "upperCI" "p_value"

Running the `validate_maraca_plot()` function on a maraca plot object returns a list with the following items:

1. `plot_type`: depending on which `density_plot_type` was selected for the plot either `GeomPoint`, `GeomViolin` and/or `GeomBoxplot`
2. `proportions`: the proportions of the HCE components
3. `tte_data`: time-to-event data if part of the step outcomes has type tte, otherwise `NULL`
4. `binary_step_data`: binary data if part of the step outcomes has type binary, otherwise `NULL`
5. `binary_step_data`: if last endpoint was binary then contains the data for the minimum, maximum and middle point x values displayed in the ellipsis, otherwise `NULL`
6. `scatter_data`: if last endpoint was continuous and plot was created with `density_plot_type = "scatter"` then contains dataset that was plotted in scatter plot, otherwise `NULL`
7. `boxstat_data`: if last endpoint was continuous and if plot was created with `density_plot_type = "box"` or `density_plot_type = "default"` then contains the boxplot statistics, otherwise `NULL`
8. `violin_data`: if last endpoint was continuous and if plot was created with `density_plot_type = "violin"` or `density_plot_type = "default"` then contains the violin distribution data, otherwise `NULL`
9. `wo_stats`: if maraca object was created with `compute_win_odds = TRUE` then contains the win odds statistics, otherwise `NULL`

These can then be converted to a convenient format for validation, such as as individual data.frames.

    library(dplyr)
    library(tidyr)

    validation_list$proportions %>%
      as.data.frame() %>%
      rename("proportion" = ".")
    ##                    proportion
    ## Outcome I                13.8
    ## Outcome II               18.1
    ## Outcome III              12.7
    ## Outcome IV                9.8
    ## Continuous outcome       45.6

    head(validation_list$tte_data)
    ##                     x   y   group
    ## geom_step.3 0.1251495 0.2 Control
    ## geom_step.4 0.1593700 0.4 Control
    ## geom_step.5 0.4625571 0.6 Control
    ## geom_step.6 0.5905571 0.2  Active
    ## geom_step.7 0.6209704 0.4  Active
    ## geom_step.8 0.6478802 0.6  Active

    validation_list$boxstat_data %>%
     unnest_wider(outliers, names_sep = "") %>%
     pivot_longer(., cols = -group, names_to = "stat_name", values_to = "values") %>%
     filter(!is.na(values)) %>%
      as.data.frame()
    ##      group     stat_name    values
    ## 1   Active whisker_lower  63.82323
    ## 2   Active   hinge_lower  74.87904
    ## 3   Active        median  79.68861
    ## 4   Active   hinge_upper  83.87907
    ## 5   Active whisker_upper  95.53322
    ## 6   Active     outliers1  60.77490
    ## 7   Active      x_lowest  60.77490
    ## 8   Active     x_highest  95.53322
    ## 9  Control whisker_lower  57.76833
    ## 10 Control   hinge_lower  70.99354
    ## 11 Control        median  75.90131
    ## 12 Control   hinge_upper  80.73315
    ## 13 Control whisker_upper  94.45711
    ## 14 Control     outliers1  54.40000
    ## 15 Control     outliers2  96.39603
    ## 16 Control     outliers3  97.24586
    ## 17 Control     outliers4  99.68534
    ## 18 Control     outliers5 100.00000
    ## 19 Control      x_lowest  54.40000
    ## 20 Control     x_highest 100.00000

    head(validation_list$violin_data)
    ##    group        x  y  density density_scaled violinwidth width
    ## 1 Active 60.77490 44 1.438990     0.01972645    44.18464 18.72
    ## 2 Active 60.84292 44 1.476413     0.02023947    44.18944 18.72
    ## 3 Active 60.91094 44 1.514659     0.02076376    44.19435 18.72
    ## 4 Active 60.97896 44 1.554340     0.02130773    44.19944 18.72
    ## 5 Active 61.04698 44 1.595229     0.02186826    44.20469 18.72
    ## 6 Active 61.11500 44 1.637199     0.02244361    44.21007 18.72

    validation_list$wo_stats
    ##      winodds      lowerCI      upperCI      p_value
    ## 1.643265e+00 1.416117e+00 1.906848e+00 9.354073e-12