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Indexed by:期刊论文

First Author:Niu, Yi

Correspondence Author:Peng, YW (reprint author), Queens Univ, Dept Publ Hlth Sci, Kingston, ON K7L 3N6, Canada.

Co-author:Wang, Xiaoguang,Peng, Yingwei

Date of Publication:2018-07-01

Journal:COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Included Journals:SCIE

Volume:161

Page Number:115-124

ISSN No.:0169-2607

Key Words:Clustered survival data; Mixture cure models; Marginal approach; Estimating equations; Expectation-Solution algorithm; R package

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 package geecure 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 package geecure 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 package geecure 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. (C) 2018 Elsevier B.V. All rights reserved.