riskRegression: selectCox – R documentation

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selectCox

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

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

riskRegression

Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

v2020.12.08

GPL (>= 2)

Authors

Thomas Alexander Gerds [aut, cre], Paul Blanche [ctb], Rikke Mortensen [ctb], Marvin Wright [ctb], Nikolaj Tollenaar [ctb], John Muschelli [ctb], Ulla Brasch Mogensen [ctb], Brice Ozenne [aut] (<https://orcid.org/0000-0001-9694-2956>)

Initial release