Overview of endpoints

endpoints is an R package for flexibly simulating clinical trial datasets with correlated endpoints of different types through a Gaussian copula framework. Centered on a single core function, makeData(), it provides a unified interface for generating continuous, binary, count, and time-to-event outcomes, while also supporting multiple treatment arms, censoring, and stochastic enrollment. The package is designed to produce realistic patient-level datasets with configurable treatment effects and dependence structures for trial planning and simulation studies.

Features

endpoints supports

• Multi-endpoint simulation of correlated clinical trial data within a unified framework
• Mixed endpoint types, including continuous, binary, count, and time-to-event outcomes
• Multiple treatment arms with arm-specific treatment effects
• Correlation control through a Gaussian copula, with optional automatic calibration to target observed Pearson correlations
• Time-to-event functionality including independent censoring, administrative censoring, and fatal/non-fatal endpoint logic for semi-competing risks settings
• Stochastic enrollment with uniform, exponential, and piecewise exponential accrual mechanisms
• Longitudinal data creation, either by treating repeated measurements as correlated endpoints or by generating correlated latent quantities for downstream longitudinal data-generating processes
• Comprehensive S3 methods for simulation objects, including print(), summary(), and plot()

Installation

Install from GitHub with:

# install.packages("remotes")
remotes::install_github("Boehringer-Ingelheim/endpoints")

Documentation

• A User Guide for the endpoints Package
• Simulating Longitudinal Data with endpoints

Quick Start

Generate a data set with three correlated endpoints, one time-to-event, one continuous and one binary:

library(endpoints)

# correlation structure across the 3 endpoints
corMat_3 <- corr_make(
  num_endpoints = 3,
  values = rbind(
    c(1, 2, 0.20),
    c(1, 3, 0.10),
    c(2, 3, 0.15)
  )
)

# endpoint specifications
cont_ep <- list(
  endpoint_type = "continuous",
  baseline_mean = 10,
  sd            = c(3, 2),
  trt_effect    = -2
)

bin_ep <- list(
  endpoint_type = "binary",
  baseline_prob = 0.30,
  trt_prob      = 0.45
)

tte_ep <- list(
  endpoint_type  = "tte",
  baseline_rate  = 1 / 24,
  trt_effect     = log(0.80),
  censoring_rate = 1 / 216,
  fatal_event    = FALSE
)

# simulate data
sim_dat <- makeData(
  correlation_matrix    = corMat_3,
  sample_size_per_group = 1000,
  SEED                  = 123,
  endpoint_details      = list(cont_ep, bin_ep, tte_ep)
)

# inspect the simulation
sim_dat
summary(sim_dat)
head(sim_dat$data)

# quick pairwise plot for arm 0
plot(sim_dat, arm = 0, names = c("Biomarker", "Response", "Time to event"))