frailtypack:Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
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 and/or longitudinal 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. 14) Competing Joint Frailty Model:
A single type of recurrent event and two terminal events. 15)
functions to compute power and sample size for four
Gamma-frailty-based designs: Shared Frailty Models, Nested
Frailty Models, Joint Frailty Models, and General Joint Frailty
Models. Each design includes two primary functions: a power
function, which computes power given a specified sample size;
and a sample size function, which computes the required sample
size to achieve a specified power. Moreover, the package can be
used with its shiny application, in a local mode or by
following the link below.
