A Pseudo-Observations Approach for Analyzing Survival Data with a Cure Fraction

A collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) . The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.

pseudoCure

pseudoCure: Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach

The pseudoCure package implements a pseudo-observation approach for survival data with a cure fraction. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard model.

Installation

Install and load the package from GitHub using

> devtools::install_github("stc04003/pseudoCure")
> library(pseudoCure)
> packageVersion("pseudoCure")

Reference

Su, C.-L., Chiou, S., Lin, F.-C., and Platt, R. W. (2022) Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach Statistical Methods in Medical Research, 31(11): 2037–2053.