Simulate Parametric and Semi-Parametric Survival Times with Object-Oriented Design

Simulate parametric and semi-parametric survival times through a consistent, reusable interface for each distribution, using an object-oriented design. Supported distributions include Exponential, Weibull, Gompertz, Log-Logistic, Log-Normal, and Piecewise Exponential. Random variates can be generated under Proportional Hazards, Accelerated Failure Time, and Extended Hazards models, as well as under renewal and non-homogeneous Poisson recurrent event processes, following the methods described by Bender (2003) and Leemis (1987) in Operations Research, 35(6), 892-894.

survobj

by John Aponte

survobj simulates survival times through a consistent, reusable interface for each distribution, using an object-oriented design. It supports the Exponential, Weibull, Gompertz, Log-Logistic, Log-Normal, and Piecewise Exponential distributions, and can generate random variates under Proportional Hazards, Accelerated Failure Time, and Extended Hazards models, as well as under renewal and non-homogeneous Poisson recurrent event processes.

It is meant for simulation studies: power calculations, sample size justification, or checking how an analysis method behaves under a known data-generating process.

Quick start

library(survobj)

# Define a Weibull SURVIVAL object from a failure proportion at a given time
obj <- s_weibull(fail = 0.4, t = 1, shape = 1.5)

# Survival, hazard and cumulative hazard at time 0.5
sfx(obj, 0.5)
hfx(obj, 0.5)
Cum_Hfx(obj, 0.5)

# Draw 10 random survival times from the baseline distribution
rsurv(obj, 10)

# Draw random survival times under a hazard ratio of 0.7
rsurvhr(obj, rep(0.7, 10))

# Plot the survival, hazard, cumulative hazard and inverse cumulative
# hazard functions
plot(obj)

Usage

It is necessary first to define a SURVIVAL object for a distribution (e.g. with s_weibull()), in order to evaluate its properties or to simulate survival times from it. This object encapsulates, in a consistent way, all the functions associated with that distribution, so the same code works regardless of the distribution chosen.

Once a SURVIVAL object is defined, it gives access to the same set of functions regardless of the underlying distribution:

• sfx(): survival function
• hfx(): hazard function
• Cum_Hfx(): cumulative hazard function
• invCum_Hfx(): inverse of the cumulative hazard function
• rsurv(): random survival times from the baseline distribution
• rsurvhr(): random survival times under a Proportional Hazards model
• rsurvaft(): random survival times under an Accelerated Failure Time model
• rsurveh(): random survival times under the Extended Hazards model (combined Proportional Hazards and Accelerated Failure Time effects)

Recurrent events (repeated episodes per subject) can be simulated under a renewal process or a non-homogeneous Poisson process:

• renewhr() / renewaft(): next episode time under a renewal process, given the previous episode time
• nhpphr() / nhppaft(): next episode time under a non-homogeneous Poisson process, given the previous episode time

A set of plotting helpers visualize simulations against the baseline distribution using Kaplan-Meier and cumulative hazard curves:

• plot(obj) / plot_survival(): plot the functions of a single SURVIVAL object
• ggplot_survival_random(): plot simulated draws from the baseline distribution
• ggplot_survival_hr() / ggplot_survival_aft() / ggplot_survival_eh(): plot simulated draws under each model, against the baseline
• compare_survival(): compare two SURVIVAL objects graphically, even from different distribution families

Distributions

SURVIVAL objects can be created using the following factory functions:

• s_exponential(): Exponential distribution
• s_weibull(): Weibull distribution
• s_gompertz(): Gompertz distribution
• s_piecewise(): Piecewise Exponential distribution
• s_loglogistic(): Log-Logistic distribution
• s_lognormal(): Log-Normal distribution

Each of these functions can be parameterized either by its canonical parameters, or by the proportion surviving/failing at a given time, which is often more natural when planning a study.

See the vignettes for worked examples of simulating survival data, including recurrent events and non-proportional hazards trials.

Installation

To install the released version from CRAN use:

install.packages("survobj")

To install the development version of this package from GitHub use:

devtools::install_github("johnaponte/survobj", build_manual = TRUE, build_vignettes = TRUE)

Learn more

https://johnaponte.github.io/survobj/