Visualization of HCE using maraca plots"
In hce: Design and Analysis of Hierarchical Composite Endpoints

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Visualization

Setup

knitr::include_graphics("hex-hce.png", dpi = 1000)

Load the package hce and check the version:

library(hce)
packageVersion("hce")

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

The Maraca Plot

The maraca plot (named for its visual similarity to the musical instrument) has been recently introduced [@karpefors2023maraca] for the 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 adjoining, from left to right, uniformly scaled Kaplan–Meier plots of times to each dichotomous outcome among those without more severe outcomes, with a superimposed box/violin plot of the continuous outcome.

The maraca plot is implemented in the maraca package [@maraca], which depends on the hce package. The maraca package includes a plot.hce() method to visualize objects of type hce. Consider the following example:

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)

knitr::include_graphics("maraca.png", dpi = 100)

The example illustrates a maraca plot with a single dishotomous outcome combined with a continuous outcome. The dischotmous outcomes over time are simulated from a Weibull distribution with shape = 0.35 in both treatment groups. The rate parameter in the active group is 10 per 100 patients per year, and 15 in the control group (100 patients per year is the default value).