geecure-package: Marginal proportional hazards mixture cure models with...

In geecure: Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations

Description

A package that uses generalized estimating equations (GEE) approach to estimate marginal proportional hazards mixture cure (PHMC) models. This package implements recently developed inference procedures for the marginal PHMC models with the expectation-solution (ES) algorithm. The package includes the parametric PHMC model with Weibull baseline distribution in the latency part and the semiparametric PHMC model for fitting the multivariate survival data with a cure fraction.

Details

Package: geecure
Type: Package
Version: 1.0-6
Date: 2018-03-28
License: GPL(>=2)
LazyData: TRUE

Author(s)

Yi Niu <[e-mail]>, Hui Song, Xiaoguang Wang, Yingwei Peng

References

Liang, K.-Y. and Zeger, S. L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73: 13-22.

Niu, Y. and Peng, Y. (2013) A semiparametric marginal mixture cure model for clustered survival data. Statistics in Medicine, 32: 2364-2373.

Niu, Y. and Peng, Y. (2014) Marginal regression analysis of clustered failure time data with a cure fraction. Journal of Multivariate Analysis, 123: 129-142.

Niu, Y., Song, L., Liu, Y, and Peng, Y. (2018) Modeling clustered long-term survivors using marginal mixture cure model. Biometrical Journal, doi: 10.1002/bjmj.201700114.

Peng, Y., Taylor, J. M. G, and Yu, B. (2007) A marginal regression model for multivariate failure time data with a surviving fraction. Lifetime Data Analysis, 13: 351-369

Rosen, O., Jiang, W., and Tanner, M. A. (2000) Mixtures of marginal models. Biometrika, 87: 391-404.

Yu, B. and Peng, Y. (2008) Mixture cure models for multivariate survival data. Computational Statistics & Data Analysis, 52: 1524-1532.

geecure documentation built on May 2, 2019, 6:03 a.m.