dataNested

This contains a  simulated sample of 400 observations which allow establishing 20 clusters with 4 subgroups and 5 subjects in each subgroup, in order to obtain two levels of grouping. This data set is useful to illustrate how to fit a nested model.
Two independent gamma frailty parameters with a variance fixed at 0.1 for the cluster effect and at 0.5 for the subcluster effect were generated. Independent survival times were generated from a simple Weibull baseline risk function. The percentage of censoring data was around 30 per cent. The right-censoring variables were generated from a uniform distribution on [1,36] and a left-trucating variable was generated with a uniform distribution on [0,10]. Observations were included only if the survival time is greater than the truncated time.

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.

Virginie Rondeau

frailtypack

General Frailty models: shared, joint and nested frailty models
        with prediction

dataNested function

V. Rondeau, L. Filleul, P. Joly (2006). Nested frailty models using maximum penalized likelihood estimation.
<em>Statistics in Medecine</em>, <b>25</b>, 4036-4052.

dataNested: Simulated data with two levels of grouping

Description

Usage

Arguments

source