Geecure: An R-package for marginal proportional hazards mixture cure models. (July 2018)

• Record Type: Journal Article
• Authors: Niu, Yi; Wang, Xiaoguang; Peng, Yingwei
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 115
• Page End: 124
• Publication Date: 2018-07
• Subjects: Clustered survival data -- Mixture cure models -- Marginal approach -- Estimating equations -- Expectation-Solution algorithm -- R package; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.017
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Survival data with a cured fraction often arise in cancer clinical studies in understanding treatment effects. Existing software packages are not suitable for correlated survival times, such as clustered survival data. geecure is an R package for fitting the marginal mixture cure models to clustered survival data with a cured fraction. geecure provides clinical researchers an easy access to the marginal mixture cure models in routine survival analysis. Abstract: Background and objective : Most of available software packages for mixture cure models to analyze survival data with a cured fraction assume independent survival times, and they are not suitable for correlated survival times, such as clustered survival data. The objective of this paper is to present a software package to fit a marginal mixture cure model to clustered survival data with a cured fraction. Methods : We developed an R packagegeecure that fits the marginal proportional hazards mixture cure (PHMC) models to clustered right-censored survival data with a cured fraction. The dependence among the cure statuses and among the survival times of uncured patients within a cluster are modeled by working correlation matrices through the generalized estimating equations, and the Expectation-Solution algorithm is used to estimate the parameters. The variances of the estimated regression parameters are estimated by either a sandwich method or a bootstrap method. Results : The packagegeecure can fit the marginal PHMC model where the cumulative baseline hazard function is either a two-parameter Weibull distribution or specified nonparametrically. Fitting the parametric PHMC model with the Weibull baseline hazard function on average takes less time than fitting the semiparametric PHMC model does. Two variance estimation methods are comparable in the simulation study. The sandwich method takes much less time than the bootstrap method in variance estimation. Conclusions : The packagegeecure provides an easy access to the marginal PHMC models for clustered survival data with a cured fraction in routine survival analysis. It is easy to use and will make the wide applications of the marginal PHMC models possible.