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 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 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