predict.parfm: Predictions of frailty values for Parametric Frailty Models

Description Usage Arguments Value Author(s) References See Also Examples

Description

The function predict.parfm() computes predictions of frailty values for objects of class parfm.

Usage

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## S3 method for class 'parfm'
predict(object, ...)

Arguments

object

A parametric frailty model, object of class parfm.

...

see predict()

Value

An object of class predict.parfm.

Author(s)

Federico Rotolo [aut, cre], Marco Munda [aut], Andrea Callegaro [ctb]

References

Glidden D, Vittinghoff E (2004). Modelling Clustered Survival Data From Multicentre Clinical Trials. Statistics in medicine, 23(3), 369–388.

Munda M, Rotolo F, Legrand C (2012). parfm: Parametric Frailty Models in R. Journal of Statistical Software, 51(11), 1-20. DOI 10.18637/jss.v051.i11

See Also

parfm

Examples

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library(parfm)
data(kidney)
kidney$sex <- kidney$sex - 1

model <- parfm(Surv(time,status) ~ sex + age, 
               cluster = "id", data = kidney,
               dist = "exponential", frailty = "gamma")
u <- predict(model)
u


# Predictions from semi-parametric Gamma frailty model
# via coxph() function
model.coxph <- coxph(Surv(time,status) ~ sex + age + 
                         frailty(id, frailty = "gamma", eps = 1e-11), 
                     outer.max = 15, data = kidney)
u.coxph <- exp(model.coxph$frail)


# Plot of predictions from both models
par(mfrow = c(1,2))
ylim <- c(0, max(c(u, u.coxph)))
plot(u, sort = "i",
     main = paste("Parametric", 
                  "Gamma frailty model",
                  "with Exponential baseline", 
                  sep = "\n"),
     ylim = ylim)

names(u.coxph) <- kidney[seq(2,76, 2), "id"]
class(u.coxph) <- "predict.parfm"
attr(u.coxph, "clustname") <- "id"
plot(u.coxph, sort = "i",
     main = paste("Semi-parametric",
                  "Gamma frailty model", sep = "\n"),
     ylim = ylim)


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