simulate_t_weibull: Generate survival data from a Weibull model

In pencal: Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival

Generate survival data from a Weibull model

Description

This function implements the algorithm proposed by Bender et al. (2005) to simulate survival times from a Weibull model. In essence, it is simply the application of the Inverse Transformation Method.

Usage

simulate_t_weibull(n, lambda, nu, X, beta, seed = 1)

Arguments

n	sample size
lambda	Weibull location parameter, positive
nu	Weibull scale parameter, positive
X	design matrix (n rows, p columns)
beta	p-dimensional vector of regression coefficients associated to X
seed	random seed (defaults to 1)

Value

A vector of survival times

Author(s)

Mirko Signorelli

References

Bender, R., Augustin, T., & Blettner, M. (2005). Generating survival times to simulate Cox proportional hazards models. Statistics in medicine, 24(11), 1713-1723.

Signorelli, M. (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. To appear in: The R Journal. Preprint: arXiv:2309.15600

Signorelli, M., Spitali, P., Al-Khalili Szigyarto, C, The MARK-MD Consortium, Tsonaka, R. (2021). Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. Statistics in Medicine, 40 (27), 6178-6196. DOI: 10.1002/sim.9178

Examples

# generate example data
set.seed(1)
n = 50
X = cbind(matrix(1, n, 1), 
   matrix(rnorm(n*9, sd = 0.7), n, 9))
beta = rnorm(10, sd = 0.7)
times = simulate_t_weibull(n = n, lambda = 1, nu = 2,
   X = X, beta = beta)
hist(times, 20)

pencal documentation built on April 3, 2025, 10:32 p.m.