Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv | Monaco | Journal of Statistical Software
Authors: John V. Monaco, Malka Gorfine, Li Hsu
Title: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv
Abstract: The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model.

Page views:: 287. Submitted: 2015-10-06. Published: 2018-09-03.
Paper: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv     Download PDF (Downloads: 89)
Supplements:
frailtySurv_1.3.5.tar.gz: R source package Download (Downloads: 3; 425KB)
v86i04.R: R replication code Download (Downloads: 5; 47KB)

DOI: 10.18637/jss.v086.i04

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.