catpredi.survival: Function to obtain optimal cut points to categorise a...
In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

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

Returns an object with the optimal cut points to categorise a continuous predictor variable in a Cox proportional hazards regression model

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catpredi.survival(formula, cat.var, cat.points = 1, data, 
method = c("addfor", "genetic"), conc.index = c("cindex", "cpe"),
range = NULL, correct.index = TRUE, control = controlcatpredi.survival(), ...)

`formula`	An object of class `formula` giving the model to be fitted in addition to the continuous covariate is aimed to categorise. The response must be a survival object as returned by the `Surv` function. This argument allows the user to specify whether the continuous predictor should be categorised in a univariable context, or in presence of other covariates or cofounders, i.e in a multiple Cox proportional hazards regression model. For instance, Surv(SurvT,SurvS)~1 indicates that the categorisation should be done in a univariable setting.
`cat.var`	Name of the continuous variable to categorise.
`cat.points`	Number of cut points to look for.
`data`	Data frame containing all needed variables.
`method`	The algorithm selected to search for the optimal cut points. "addfor" if the AddFor algorithm is choosen and "genetic" otherwise.
`conc.index`	The concordance probability estimator selected for maximisation purposes. "cindex" if the c-index concordance probability is choosen and "cpe" otherwise. The c-index and CPE are estimated using the `rms` and `CPE` packages, respectively.
`range`	The range of the continuous variable in which to look for the cut points. By default NULL, i.e, all the range.
`correct.index`	A logical value. If TRUE the bias corrected concordance probability is estimated.
`control`	Output of the controlcatpredi.survival() function.
`...`	Further arguments for passing on to the function `genoud` of the package `rgenoud`.

Returns an object of class "catpredi.survival" with the following components:

`call`	the matched call.
`method`	the algorithm selected in the call.
`formula`	an object of class `formula` giving the model to be fitted in addition to the continuous covariate is aimed to categorise.
`cat.var`	name of the continuous variable to categorise.
`data`	the data frame with the variables used in the call.
`correct.index`	The logical value used in the call.
`results`	a list with the estimated cut points, concordance probability and bias corrected concordance probability.
`control`	the control parameters used in the call.

When the c-index concordance probability is choosen, a list with the following components is obtained for each of the methods used in the call:

`"cutpoints"`	Estimated optimal cut points.
`"Cindex"`	Estimated c-index.
`"Cindex.cor"`	Estimated bias corrected c-index.

When the CPE concordance probability is choosen, a list with the following components is obtained for each of the methods used in the call:

`"cutpoints"`	Estimated optimal cut points.
`"CPE"`	Estimated CPE.
`"CPE.cor"`	Estimated bias corrected CPE.

Irantzu Barrio and Maria Xose Rodriguez-Alvarez

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

M Gonen and G Heller (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92:965-970.

F Harrell (2001). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.

library(CatPredi)
library(survival)
set.seed(123)
#Simulate data
  n = 500
  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)
  
  # Select optimal cut points using the AddFor algorithm
  res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2,
   data = dat, method = "addfor", conc.index = "cindex", range = NULL, 
   correct.index = FALSE)