CRAN/E | frailtypack

frailtypack

Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

Installation

About

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Citation frailtypack citation info
virginie1rondeau.wixsite.com/virginierondeau/software-frailtypack https://frailtypack-pkg.shinyapps.io/shiny_frailtypack/
virginie1rondeau.wixsite.com/virginierondeau/software-frailtypack https://frailtypack-pkg.shinyapps.io/shiny_frailtypack/

Key Metrics

Version 3.5.0
R ≥ 2.10.0
Published 2021-12-20 708 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks frailtypack results

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Maintainer

Maintainer

Virginie Rondeau

virginie.rondeau@u-bordeaux.fr

Authors

Virginie Rondeau
Juan R. Gonzalez
Yassin Mazroui
Audrey Mauguen
Amadou Diakite
Alexandre Laurent
Myriam Lopez
Agnieszka Krol
Casimir L. Sofeu
Julien Dumerc
Denis Rustand
Jocelyn Chauvet
Quentin Le Coent

Material

NEWS
Reference manual
Package source

In Views

Survival

Vignettes

Package summary

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

frailtypack archive

Depends

R ≥ 2.10.0
survival
boot
MASS
survC1
doBy

Imports

statmod
nlme
shiny
rootSolve
splines

Suggests

knitr
testthat
rmarkdown
markdown

Reverse Suggests

extrafrail
icensBKL