tenf.crr: Ten fold cross validation for competing risks regression
In jixccf/QHScrnomo: Construct nomograms for competing risk models

Description Usage Arguments Details Value Note Author(s) See Also Examples

Ten fold cross validation for crr endpoint

1	tenf.crr(fit, time = NA, lps = FALSE, fold = 10)

`fit`	a competing risks regression model fittd by function `crr.fit`.
`time`	the expected time point.
`lps`	logical flag. If true, values of predicted X beta will be output instead of cumulative incidence
`fold`	number of fold. the default is 10 fold cross validation.

Do cross validation on a competing risk regression model.

A vector of predicted values of cumulative incidence or X beta for each observation.

Before the function is called, packages 'Hmisc', 'rms' and 'cmprsk' should be loaded as the function will call some funcitons in these packages.

Changhong Yu, Michael Kattan, Ph.D
Department of Quantitative Health Sciences
Cleveland Clinic

crr.fit, crr

data(prostate.dat)
dd <- datadist(prostate.dat)
options(datadist = "dd")
prostate.f <- cph(Surv(TIME_EVENT,EVENT_DOD == 1) ~ TX  + rcs(PSA,3) +
           BX_GLSN_CAT +  CLIN_STG + rcs(AGE,3) +
           RACE_AA, data = prostate.dat,
           x = TRUE, y= TRUE, surv=TRUE,time.inc = 144)
prostate.crr <- crr.fit(prostate.f,cencode = 0,failcode = 1)

## ten fold cross validation
prostate.dat$preds.tenf<-
    tenf.crr(prostate.crr,time = 120)