R: Backward variable selection in the Cox regression model

selectCox {pec} | R Documentation

## Backward variable selection in the Cox regression model

### Description

This is a wrapper function which first selects variables in the Cox regression model using `fastbw` from the `rms` package and then returns a fitted Cox regression model with the selected variables.

### Usage

    selectCox(formula, data, rule = "aic")

### Arguments

`formula` | A formula object with a `Surv` object on the left-hand side and all the variables on the right-hand side.
`data` | Name of an data frame containing all needed variables.
`rule` | The method for selecting variables. See `fastbw` for details.

### Details

This function first calls `cph` then `fastbw` and finally `cph` again.

### References

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL http://www.jstatsoft.org/v50/i11/.

### Examples

    data(GBSG2)
    library(survival)
    f <- selectCox(Surv(time,cens)~horTh+age+menostat+tsize+tgrade+pnodes+progrec+estrec ,
    	       data=GBSG2)

[Package pec version 2019.11.03 Index]