CatPredi (version 1.4)

comp.cutpoints.survival: Selection of optimal number of cut points

Description

Compares two objects of class "catpredi.survival"

Usage

comp.cutpoints.survival(obj1, obj2, V = 100)

Value

This function returns an object of class "comp.cutpoints.survival" with the following components:

CI.cor.diff

the difference of the bias corrected concordance probability for the two categorical variables.

icb.CI.diff

bootstrap based confidence interval for the bias corrected concordance probability difference.

Arguments

obj1

an object inheriting from class "catpredi.survival" for k number of cut points

obj2

an object inheriting from class "catpredi.survival" for k+1 number of cut points

V

Number of bootstrap resamples. By default V=100

Author

Irantzu Barrio and Maria Xose Rodriguez-Alvarez

References

I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92

See Also

See Also as catpredi.survival.

Examples

library(CatPredi)
library(survival)
set.seed(123)

#Simulate data
  n = 300
  tauc = 1
  X <- rnorm(n=n, mean=0, sd=2)
  SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1))   + rnorm(n, mean=0, sd=0.25)
  # Censoring time
  CensTime <- runif(n=n, min=0, max=tauc)
  # Status
  SurvS <- as.numeric(SurvT <= CensTime)
  # Data frame
  dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS)
  # \donttest{
  # Select 2 optimal cut points using the AddFor algorithm. Correct the c-index
  res.k2 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, 
  data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) 
  # Select 3 optimal cut points using the AddFor algorithm. Correct the c-index
  res.k3 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 3, 
  data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) 
    # Select optimal number of cut points
  comp <-  comp.cutpoints.survival(res.k2, res.k3, V = 100)
  # }
  # \dontshow{
  # Select 2 optimal cut points using the AddFor algorithm. Correct the c-index
  res.k2 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 1, 
  data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE, 
  control=controlcatpredi.survival(grid=20)) 
  # Select 3 optimal cut points using the AddFor algorithm. Correct the c-index
  res.k3 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, 
  data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE, 
  control=controlcatpredi.survival(grid=20)) 
  # Select optimal number of cut points
  comp <-  comp.cutpoints.survival(res.k2, res.k3, V = 2)
  # }