cpsurvsim: An R Package for Simulating Data from Change-Point Hazard Distributions

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.

Camille J. Hochheimer, PhD (Department of Biostatistics and Informatics) , Roy T. Sabo, PhD (Department of Biostatistics)
2022-06-21

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-005.zip

References

Reuse

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 ...".

Citation

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}
}