dataAdditive

This contains simulated samples of 100 clusters with 100 subjects in each cluster, like a gathering of clinical trials databases. Two correlated centered gaussian random effects are generated with the same variance fixed at 0.3 and the covariance at -0.2. The regression coefficient $\beta$ is fixed at -0.11. The percentage of right-censored data is around 30 percent which are generated from a uniform distribution on [1,150]. The baseline hazard function is considered as a simple Weibull.

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

dataAdditive function

V. Rondeau, S. Michiels, B.Liquet, and J.P. Pignon (2008). Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by mean of the maximum penalized likelihood approach.
<em>Statistics in Medecine</em>, <b>27</b>, 1894-1910.

dataAdditive: Simulated data as a gathering of clinical trials databases

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