# Maraca Plots - Introduction to the Mosaic plot

#### Monika Huhn

#### 28/05/2025

## Introduction

The mosaic plot is a new type of graph introduced in the package that provides an easy overview over the different components (losses, wins and ties) between the active treatment arm vs the control for the different endpoints.

An example plot is shown above. The plot presents a mosaic of rectangles, each outlined with white borders, reflecting the comparative outcomes between an active treatment group and a control group, as indicated by axis labels (active on the y-axis, control on the x-axis).

Each rectangle corresponds to a pair of outcomes, displaying the proportion of each treatment group achieving a particular endpoint. For instance, a rectangle labeled “Outcome I” on the y-axis and “Outcome II” on the x-axis represents a “Loss” for the active group, as these patients have reached a higher-level endpoint compared to those in the control group. Conversely, when “Outcome II” on the active group (y-axis) is compared to “Outcome I” in the control group (x-axis), it signifies a “Win” for the active group, where active group patients attain a lower-level endpoint than the control group.

Diagonal rectangles, such as those representing “Outcome I” vs “Outcome I”, illustrate tied components between the groups over the trial period. While these rectangles reflect identical outcomes across groups, a diagonal line across the plot shows cumulative event timing. Events in the active group cause an upward shift in this line, while events in the control group shift it to the right. The line scales with trial time, maintaining continuity until the last recorded outcome.

For the last outcome, the diagonal line is scaled between the lowest and highest results across both groups. The line depicts cumulative results for patients, ordered from lowest to highest (or highest to lowest if lower values are considered as “better”). As each patient’s result is considered, the line jumps upwards or to the right, reflecting the ordered accumulation of results from both the active and control groups.

Note if any of the endpoints are binary there is no way of ordering the individual patients either temporally or by value, so a diagonal line is displayed for this endpoint component.

## Creating the mosaic plot

Let us first read in some data.

    library(maraca)

    data(hce_scenario_a)

In order to use the `mosaic_plot`, we have to first create a `maraca` object.

    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")
    )

Now we can just plot the object using the `mosaic_plot()` function.

    mosaic_plot(maraca_dat)

The component tie rectangles can also be specifically highlighted using the flag `highlight_ties = TRUE`.

    mosaic_plot(maraca_dat, highlight_ties = TRUE)

If you calculated the win odds when creating your maraca object (`compute_win_odds = TRUE`), you can also display the winning probability for the active arm in the plot by using the flag `win_prob = TRUE`.

    maraca_with_win_odds <- 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
    )
    mosaic_plot(maraca_with_win_odds, win_prob = TRUE)

You can also hide the diagonal line (highlighting even Win/Loss split) to compare against using the flag `diagonal_line = FALSE`

    mosaic_plot(maraca_dat, diagonal_line = FALSE)

It is also possible to use the `mosaic_plot()` function directly on an `adhce` object (created using the hce package).

    library(hce)

    Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1)
    Rates_P <- c(2.47, 2.24, 2.9, 4, 6)

    hce_dat <- simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P,
                      CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3,
                      seed = 31337)

    mosaic_plot(hce_dat)

## Styling

The resulting plot for the `mosaic_plot()` functions is a normal ggplot2 object that can be styled accordingly.

    mosaic_plot(maraca_dat) +
      ggplot2::scale_fill_manual(values = c("red", "seagreen"), name = NULL)

For the users convenience, there are also different themes available to style the plot.

The default style is called `theme = "maraca"`.

    mosaic_plot(maraca_dat, theme = "maraca")

There are 2 different themes with different color schemes, `theme = "color1"` and `theme = "color2"`.

    mosaic_plot(maraca_dat, theme = "color1")

    mosaic_plot(maraca_dat, theme = "color2")

There is also a theme without any styling `theme = "none"` that can be used as a base when the user wants to style the plot themselves.

    mosaic_plot(maraca_dat, theme = "none")