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 lognormal 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 semicompeting 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 failuretime endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal twopart joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailtycopula models for the validation of surrogate endpoints in multiple randomized clinical trials with failuretime 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 typevarying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Lefttruncated (not for Joint model), rightcensored data, intervalcensored 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 timevarying covariates effects in Cox, shared and joint frailty models (15). 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.
Package details 


Author  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 
Maintainer  Virginie Rondeau <virginie.rondeau@ubordeaux.fr> 
License  GPL (>= 2.0) 
Version  3.5.0 
URL  https://virginie1rondeau.wixsite.com/virginierondeau/softwarefrailtypack https://frailtypackpkg.shinyapps.io/shiny_frailtypack/ 
Package repository  View on CRAN 
Installation 
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