We describe the penPHcure R package, which implements the semiparametric proportional-hazards (PH) cure model of Sy and Taylor (2000) extended to time-varying covariates and the variable selection technique based on its SCAD-penalized likelihood proposed by Beretta and Heuchenne (2019). In survival analysis, cure models are a useful tool when a fraction of the population is likely to be immune from the event of interest. They can separate the effects of certain factors on the probability of being susceptible and on the time until the occurrence of the event. Moreover, the penPHcure package allows the user to simulate data from a PH cure model, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates, with a method similar to Hendry (2014). We present the results of a simulation study to assess the finite sample performance of the methodology and illustrate the functionalities of the penPHcure package using criminal recidivism data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-061.zip
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For attribution, please cite this work as
Beretta & Heuchenne, "The R Journal: penPHcure: Variable Selection in Proportional Hazards Cure Model with Time-Varying Covariates", The R Journal, 2021
BibTeX citation
@article{RJ-2021-061,
author = {Beretta, Alessandro and Heuchenne, Cédric},
title = {The R Journal: penPHcure: Variable Selection in Proportional Hazards Cure Model with Time-Varying Covariates},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-061},
doi = {10.32614/RJ-2021-061},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {53-66}
}