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:

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).



To install the latest release from CRAN:



To install the latest devel version from GitHub:




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.

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.

Sy, Judy P, and Jeremy MG Taylor. 2000. “Estimation in a Cox Proportional Hazards Cure Model.” Biometrics 56 (1): 227–36.