Getting Started with simtte

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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[@]metanet.ch.

Introduction

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

Weibull parametric survival models
Flexible M-spline baseline hazard models (Royston-Parmar style)

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

Bender R, Augustin T, Blettner M (2005). Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine, 24(11), 1713-1723.
Royston P, Parmar MKB (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data. Statistics in Medicine, 21(15), 2175-2197.

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.