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

View source: R/catpredi.survival.R

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

1 2 3 | ```
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 |

`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 |

`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 |

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 |

`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.

See Also `controlcatpredi.survival`

, `comp.cutpoints.survival`

, `plot.catpredi.survival`

, `catpredi`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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)
``` |

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