frailtypack: General Frailty Models: Shared, Joint and Nested Frailty Models
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 joint modelling for recurrent events
with terminal event for clustered data or not.
5) Joint General frailty models in the context of a joint modelling for recurrent
events with terminal event data with two independent frailty terms.
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 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. Now, you can also consider time-varying covariates
effects in Cox, shared and joint models. The package includes concordance measures
for Cox proportional hazards models and for shared frailty models.