dataCox: Cox Proportional Hazards Model Data Generation From Weibull...

In coxphSGD: Stochastic Gradient Descent log-Likelihood Estimation in Cox Proportional Hazards Model

Description

Function dataCox generaters random survivaldata from Weibull distribution (with parameters lambda and rho for given input x data, model coefficients beta and censoring rate for censoring that comes from exponential distribution with parameter cens.rate.

Usage

dataCox(n, lambda, rho, x, beta, cens.rate)

Arguments

n	Number of observations to generate.
lambda	lambda parameter for Weibull distribution.
rho	rho parameter for Weibull distribution.
x	A data.frame with an input data to generate the survival times for.
beta	True model coefficients.
cens.rate	Parameter for exponential distribution, which is responsible for censoring.

Details

For each observation true survival time is generated and a censroing time. If censoring time is less then survival time, then the survival time is returned and a status of observations is set to 0 which means the observation had censored time. If the survival time is less than censoring time, then for this observation the true survival time is returned and the status of this observation is set to 1 which means that the event has been noticed.

Value

A data.frame containing columns:

• id an integer.

• time survival times.

• status observation status (event occured (1) or not (0)).

• x a data.frame with an input data to generate the survival times for.

References

http://onlinelibrary.wiley.com/doi/10.1002/sim.2059/abstract

Generating survival times to simulate Cox proportional hazards models, 2005 by Ralf Bender, Thomas Augustin, Maria Blettner.

Examples

## Not run: 
x <- matrix(sample(0:1, size = 20000, replace = TRUE), ncol = 2)
dataCox(10^4, lambda = 3, rho = 2, x,
beta = c(1,3), cens.rate = 5) -> dCox

## End(Not run)

Example output

Loading required package: survival

coxphSGD documentation built on May 1, 2019, 6:32 p.m.