---
title: "Maraca Plots - Introduction to the Mosaic plot"
author: "Monika Huhn"
date: "28/05/2025"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Maraca Plots - Introduction to the Mosaic plot}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
library(dplyr)
library(ggplot2)
library(maraca)
```
## 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.
```{r fig.width=7, fig.height=6, echo = FALSE}
data(hce_scenario_b)
maraca_dat <- maraca(
data = hce_scenario_b,
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")
)
mosaic_plot(maraca_dat, theme = "color2")
```
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.
```{r maraca1, eval = TRUE}
library(maraca)
data(hce_scenario_a)
```
In order to use the `mosaic_plot`, we have to first create a
`maraca` object.
```{r}
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.
```{r fig.width=7, fig.height=6}
mosaic_plot(maraca_dat)
```
The component tie rectangles can also be specifically highlighted using the
flag `highlight_ties = TRUE`.
```{r fig.width=7, fig.height=6}
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`.
```{r fig.width=7, fig.height=6}
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`
```{r fig.width=7, fig.height=6}
mosaic_plot(maraca_dat, diagonal_line = FALSE)
```
It is also possible to use the `mosaic_plot()` function directly on
an `hce` object (created using the
[hce package](https://cran.r-project.org/package=hce)).
```{r fig.width=7, fig.height=6}
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.
```{r fig.width=7, fig.height=6}
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"`.
```{r fig.width=7, fig.height=6}
mosaic_plot(maraca_dat, theme = "maraca")
```
There are 2 different themes with different color
schemes, `theme = "color1"` and `theme = "color2"`.
```{r fig.width=7, fig.height=6}
mosaic_plot(maraca_dat, theme = "color1")
```
```{r fig.width=7, fig.height=6}
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.
```{r fig.width=8, fig.height=6}
mosaic_plot(maraca_dat, theme = "none")
```