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geecure (version 1.0-6)

Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations

Description

Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) .

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Version

Install

install.packages('geecure')

Monthly Downloads

169

Version

1.0-6

License

GPL (>= 2)

Maintainer

Yi Niu

Last Published

April 1st, 2018

Functions in geecure (1.0-6)

initial_Lambda

Initial value of the cumulative baseline hazard function
es

Expectation-Solution (ES) algorithm
geebt

Generalized estimating equations for the latency part
basesurv

Estimation of the baseline survival function
emes

Expectation-Maximization (EM) algorithm and Expectation-Solution (ES) algorithm
geecure

Marginal proportional hazards mixture cure model with generalzied estimating equations
geecure-package

Marginal proportional hazards mixture cure models with generalzied estimating equations
geega

Generalized estimating equations for the incidence part
geecure2

Semiparametric marginal proportional hazards mixture cure model
varest2

Variance estimate with sandwich formula based on the EM algorithm
print.geecure

Print geecure object
print.geecure2

Print geecure2 object
varest

Variance estimate with sandwich formula based on the ES algorithm
bmt

Bone marrow transplantation data
tonsil_bootsample

A bootstrap sample for tonsil data
smoking

A Smoking Cessation Data
tonsil

Multi-Center Clinical Trial of Tonsil Carcinoma