diabetes

The diabetes dataframe is based on a study conducted at the Steno Memorial Hospital in Denmark from 1933-1984. 
The data involve 731 patients (454 males and 277 females) and have 595 exact, 136 interval-censored and no right-censored observations.

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. 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. Prediction
        values are available. Left truncated, right-censored data,
        interval-censored data (only for Cox proportional hazard and
        shared frailty model) and strata (max=2) are allowed. The
        package includes concordance measures for Cox proportional
        hazards models and for shared frailty models.

Virginie Rondeau

frailtypack

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

diabetes function

Kindly provided by Professor P.K. Andersen.

diabetes: Interval-censored data for time from onset of diabetes to the onset of diabetic nephronpathy

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