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:
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estimate the regression coefficients and the baseline hazard function (conditional on being susceptible);
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compute confidence intervals for the estimated regression coefficients using the basic/percentile bootstrap method;
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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
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If you discover a bug or you have a suggestion to improve the package: https://github.com/a-beretta/penPHcure/issues
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For help or more information contact: a.beretta@uliege.be
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.
