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