survival

ObjFrailty

Let t be a continuous variable, we determine the value of the survival function to t after run fit.

Frailtypack now 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 (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 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
        (max=2) 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. The package includes concordance
        measures for Cox proportional hazards models and for shared
        frailty models.

survival: Survival function

Description

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Examples