Package: penPHcure 1.0.2

Alessandro Beretta

penPHcure: Variable Selection in PH Cure Model with Time-Varying Covariates

Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.

Authors:Alessandro Beretta [aut, cre], Cédric Heuchenne [aut]

penPHcure_1.0.2.tar.gz
penPHcure_1.0.2.tar.gz(r-4.5-noble)penPHcure_1.0.2.tar.gz(r-4.4-noble)
penPHcure_1.0.2.tgz(r-4.4-emscripten)penPHcure_1.0.2.tgz(r-4.3-emscripten)
penPHcure.pdf |penPHcure.html✨
penPHcure/json (API)
NEWS

# Install 'penPHcure' in R:
install.packages('penPHcure', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/a-beretta/penphcure/issues0 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

1.70 score 188 downloads 2 exports 8 dependencies

Last updated 5 years agofrom:c0b323c358. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 09 2025
R-4.5-linux-x86_64NOTEMar 09 2025
R-4.4-linux-x86_64NOTEMar 09 2025

Exports:penPHcurepenPHcure.simulate

Dependencies:latticeMASSMatrixrbibutilsRcppRcppArmadilloRdpacksurvival

Citation

To cite package ‘penPHcure’ in publications use:

Beretta A, Heuchenne C (2019). penPHcure: Variable Selection in PH Cure Model with Time-Varying Covariates. R package version 1.0.2, https://CRAN.R-project.org/package=penPHcure.

Corresponding BibTeX entry: 

  @Manual{,
    title = {penPHcure: Variable Selection in PH Cure Model with
      Time-Varying Covariates},
    author = {Alessandro Beretta and Cédric Heuchenne},
    year = {2019},
    note = {R package version 1.0.2},
    url = {https://CRAN.R-project.org/package=penPHcure},
  }

Readme and manuals

penPHcure

Contrary to standard survival analysis models, which rely on the assumption that the entire population will eventually experience the event of interest, mixture cure models allow to split the population in susceptible and non-susceptible (cured) individuals.

In this R package, we implement the semi-parametric proportional-hazards (PH) cure model of Sy and Taylor (2000) extended to time-varying covariates. In particular, the penPHcure function allows to:

  • estimate the regression coefficients and the baseline hazard function (conditional on being susceptible);

  • compute confidence intervals for the estimated regression coefficients using the basic/percentile bootstrap method;

  • perform variable selection based on the SCAD-penalized likelihood, as in Beretta and Heuchenne (2019).

Moreover, the penPHcure.simulate function allows 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, using a method similar to the one described in Hendry (2014).

Installation

CRAN

To install the latest release from CRAN:

install.packages(penPHcure)
GitHub

To install the latest devel version from GitHub:

library(devtools)
install_github("a-beretta/penPHcure")

Contact

References

Beretta, Alessandro, and Cédric Heuchenne. 2019. “Variable Selection in Proportional Hazards Cure Model with Time-Varying Covariates, Application to Us Bank Failures.” Journal of Applied Statistics 46 (9): 1529–49. https://doi.org/10.1080/02664763.2018.1554627.

Hendry, David J. 2014. “Data Generation for the Cox Proportional Hazards Model with Time-Dependent Covariates: A Method for Medical Researchers.” Statistics in Medicine 33 (3): 436–54. https://doi.org/10.1002/sim.5945.

Sy, Judy P, and Jeremy MG Taylor. 2000. “Estimation in a Cox Proportional Hazards Cure Model.” Biometrics 56 (1): 227–36. https://doi.org/10.1111/j.0006-341X.2000.00227.x.