General semiparametric shared frailty model: Estimation and simulation with frailtysurv

John V. Monaco, Malka Gorfine, Li Hsu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish
JournalJournal of Statistical Software
Volume86
DOIs
StatePublished - 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, American Statistical Association. All rights reserved.

Funding

The authors would like to thank Google, which partially funded development of frailtySurv through the 2015 Google Summer of Code, and NIH grants (R01CA195789 and P01CA53996).

FundersFunder number
National Institutes of HealthR01CA195789, P01CA53996
Google

    Keywords

    • Clustered data
    • Frailtysurv
    • R
    • Shared frailty model
    • Survival analysis

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