---
title: "Visualization of HCE using maraca plots"
date: "`r format(as.Date('2023-11-01'), '%d %B, %Y')`"
author:
  - name: "Samvel B. Gasparyan"
    affiliation: https://gasparyan.co/
output:
  rmarkdown::html_document:
          theme: "darkly"
          highlight: "zenburn"
          toc: true
          toc_float: true
          link-citations: true 
bibliography: REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{maraca}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Visualization


### Setup

```{r echo=FALSE}
knitr::include_graphics("hex-hce.png", dpi = 500)
```

Load the package `hce` and check the version

```{r setup}
library(hce)
packageVersion("hce")
```

For citing the package run `citation("hce")` [@hce].

### The maraca plot

The *maraca* plot (named after its visual similarity to its namesake musical instrument) has been recently introduced [@karpefors2023maraca] for visualization of HCEs which combine multiple dichotomous outcomes with a single continuous endpoint. The maraca plot visualizes the contribution of components of a hierarchical composite endpoint (HCE) over time. It is formed by end-to-end adjoining, from left to right by declining severity of uniformly scaled Kaplan–Meier plots of times to each dichotomous outcome among those without more severe outcomes, with superimposed box/violin plot of the continuous outcome.

The maraca plot is implemented in the package @maraca which depends on the package `hce`. The maraca package has a `plot.hce()` method to visualize objects of the type `hce`. Consider the following example.

```{r eval=FALSE}
library(maraca)
Rates_A <- 10
Rates_P <- 15
dat <- simHCE(n = 1000, n0 = 500, TTE_A = Rates_A, TTE_P = Rates_P, 
              CM_A = 0.2, CM_P = 0, seed = 2, shape = 0.35)
plot(dat)
```

```{r echo=FALSE}
knitr::include_graphics("maraca.png", dpi = 100)
```

## References