dataJoint

This contains simulated data with recurrent events and time until last follow-up that is related to the recurrent process. This data set is useful to illustrate how to fit a joint model where the censoring mechanism is informative about the recurring process.
This database combines different observations for each subject (sample size=500, number of lines=1053 so in average more than two recurrent events per subject). The right-censored case is treated only and the calendar timescale representation is used with delayed entry. Exponential death times and recurring times are generated using a gamma frailty model. The common frailty term is iid $\Gamma(2,2)$ and $\alpha$, the coefficient associated with the frailty term in the death time hazard function, is set at 0. The right-censoring variable is set at 0.8 and the two covariates are generated from a Bernoulli distribution (p=0.5).

datasets

Frailtypack now fits several classes of frailty models
        using a penalized likelihood estimation on the hazard function
        but also a parametrical estimation. 1) A shared gamma frailty
        model and Cox proportional hazard model. Left truncated,
        censored data and strata (max=2) are allowed.  Clustered and
        recurrent survival times can be studied (the
        Andersen-Gill(1982) approach has been implemented for recurrent
        events). An automatic choice of the smoothing parameter is
        possible using an approximated cross-validation procedure. 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 of recurrent events with terminal event.

Virginie Rondeau

frailtypack

General Frailty models using a semi_parametrical penalized
        likelihood estimation or a parametrical estimation

dataJoint function

V. Rondeau, S. Mathoulin-Pellissier, H. Jacqmin-Gadda, V. Brouste, P. Soubeyran (2007). Joint frailty models for recurring events and death using maximum penalized likelihood estimation:application on cancer events.
<em>Biostatistics</em>, <b>8</b>,4, 708-721.

dataJoint: Simulated data with recurrent events and informative terminal event

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