dataMultiv

This contains a simulated sample of of 800 subjects and 1652 observations. This dataset can be used to illustrate how to fit a joint multivariate frailty model.
Two gaussian correlated random effects were generated with mean 0, variances 0.5 and a correlation coefficient equals to 0.5. The coefficients $\alpha_1$ and $\alpha_2$ were fixed to 1. The three baseline hazard functions followed a Weibull distribution and right censoring was fixed at 5.

datasets

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.

dataMultiv: Simulated data for two types of recurrent events and a terminal event

Description

Usage

Arguments