General Frailty models using a semi_parametric penalized
likelihood estimation or a parametric estimation
Frailtypack now fits several classes of frailty models
using a penalized likelihood estimation on the hazard function
but also a parametric estimation. 1) A shared gamma frailty
model and Cox proportional hazard model. Clustered and
recurrent survival times can be studied (the
Andersen-Gill(1982) approach has been implemented for recurrent
events). An automatic choice of the smoothing parameter is
possible using an approximated cross-validation procedure. 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 joint
modelling for recurrent events with terminal event for
clustered data or not. Prediction values are available. Left
truncated (not for Joint model), right-censored data,
interval-censored data (only for Cox proportional hazard and
shared frailty model) and strata (max=2) are allowed. The
package includes concordance measures for Cox proportional
hazards models and for shared frailty models.

General Frailty models using a semiparametric penalized
likelihood estimation or a parametric estimation
Frailtypack now fits several classes of frailty models
using a penalized likelihood estimation on the hazard function
but also a parametric estimation. 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 (the Andersen-Gill(1982) approach has been
implemented for recurrent events). An automatic choice of the
smoothing parameter is possible using an approximated
cross-validation procedure. 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 joint modelling for recurrent
events with terminal event for clustered data or not.
Prediction values are available. Left truncated (not for Joint
model), right-censored data, interval-censored data (only for
Cox proportional hazard and shared frailty model) and strata
(max=2) are allowed. In each model, the random effects have a
gamma distribution, but you can switch to a log-normal in
shared and joint models. The package includes concordance
measures for Cox proportional hazards models and for shared
frailty models.