Published
June 20, 2022
Received
Feb 22, 2021
Change-point hazard models have several practical applications, including modeling processes such as cancer mortality rates and disease progression. While the inverse cumulative distribution function (CDF) method is commonly used for simulating data, we demonstrate the shortcomings of this approach when simulating data from change-point hazard distributions with more than a scale parameter. We propose an alternative method of simulating this data that takes advantage of the memoryless property of survival data and introduce the R package cpsurvsim which implements both simulation methods. The functions of cpsurvsim are discussed, demonstrated, and compared.
June 20, 2022
Feb 22, 2021
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-005.zip
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
PhD & PhD, "The R Journal: cpsurvsim: An R Package for Simulating Data from Change-Point Hazard Distributions", The R Journal, 2022
BibTeX citation
@article{RJ-2022-005,
author = {PhD, Camille J. Hochheimer, and PhD, Roy T. Sabo,},
title = {The R Journal: cpsurvsim: An R Package for Simulating Data from Change-Point Hazard Distributions},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-005},
doi = {10.32614/RJ-2022-005},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {196-207}
}