summary.jointPenal

print.summary.jointPenal

output from a call to frailtyPenal for joint models

object

significance level of confidence interval. Default is 95%.

level

the desired number of digits after the decimal point. Default of 6 digits is used.

This function returns hazard rations (HR) and its confidence intervals

methods

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. 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. 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.

summary.jointPenal: summary of parameter estimates of a joint frailty model

Description

Usage

Arguments

Value

See Also

Examples