The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Getting Started with simtte

Introduction

The simtte package simulates time-to-event (survival) datasets for clinical trial design and analysis. It supports:

Event times are generated using inverse transform sampling from the cumulative hazard function, computed via the mrgsolve ODE solver backend.

Statistical Framework

Weibull Model

The Weibull hazard function is:

\[h(t) = \lambda \cdot \gamma \cdot t^{\gamma - 1}\]

where \(\lambda = \exp(\mu + \mathbf{x}'\boldsymbol{\beta})\) is the scale and \(\gamma\) is the shape parameter.

M-Spline Model

For the flexible model, the baseline hazard is represented as a linear combination of M-spline basis functions, allowing complex hazard shapes.

Inverse Transform Sampling

Given a survival function \(S(t)\), we draw \(U \sim \text{Uniform}(0, 1)\) and find the time \(t^*\) such that \(S(t^*) = U\). The package solves the Kolmogorov forward equation numerically via mrgsolve and then applies this sampling scheme.

Basic Workflow

library(simtte)

Weibull Example

set.seed(42)
lp <- matrix(rnorm(50, 0, 0.5), nrow = 50)
result <- sim_tte(
  pi = lp,
  mu = -1,
  coefs = 1.1,
  time = seq(0.1, 100, by = 0.1),
  type = "weibull",
  end_time = 100
)
head(result)

M-Splines Example

data("ms_data")
lp <- matrix(runif(nrow(ms_data$basis)), nrow = nrow(ms_data$basis))
result <- sim_tte(
  pi = lp,
  mu = ms_data$mu,
  basis = ms_data$basis,
  coefs = ms_data$coefs,
  time = ms_data$time,
  type = "ms"
)
head(result)

Output Structure

The output is a data frame with columns:

Column Description
sim_time Simulated event or censoring time
sim_status Event indicator (1 = event, 0 = censored)
ID Subject identifier
lp Linear predictor (log hazard ratio)

References

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.