Load the package hce
and check the version:
For citing the package, run citation("hce")
(Gasparyan 2024).
The maraca plot (named for its visual similarity to the musical instrument) has been recently introduced (Karpefors, Lindholm, and Gasparyan 2023) 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
(Martin Karpefors, Samvel B. Gasparyan, and
Monika Huhn 2024), 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)
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).