CRAN/E | simsurv

simsurv

Simulate Survival Data

Installation

About

Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper doi:10.18637/jss.v097.i03. Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) or Crowther and Lambert (2013) doi:10.1002/sim.5823).

Citation simsurv citation info
Bug report File report

Key Metrics

Version 1.0.0
R ≥ 3.3.0
Published 2021-01-13 1198 days ago
Needs compilation? no
License GPL (≥ 3)
License File
CRAN checks simsurv results

Downloads

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Maintainer

Maintainer

Sam Brilleman

sam.brilleman@gmail.com

Authors

Sam Brilleman

cre / aut / cph

Alessandro Gasparini

ctb

Material

NEWS
Reference manual
Package source

In Views

Survival

Vignettes

Technical background to the simsurv package
How to use the simsurv package

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

simsurv archive

Depends

R ≥ 3.3.0

Imports

methods
stats

Suggests

BB ≥ 2014.10.1
eha ≥ 2.4.5
flexsurv ≥ 1.1.0
knitr ≥ 1.15.1
MASS
rmarkdown
rstpm2 ≥ 1.4.1
survival ≥2.40.1
testthat ≥ 1.0.2

Reverse Depends

TwoArmSurvSim

Reverse Suggests

BayesSurvival
KMunicate