# rcorrp.cens

##### Rank Correlation for Paired Predictors with a Possibly Censored Response, and Integrated Discrimination Index

Computes U-statistics to test for whether predictor `X1` is more
concordant than predictor `X2`, extending `rcorr.cens`

. For
`method=1`

, estimates the fraction of pairs for which the
`x1`

difference is more impressive than the `x2`

difference. For `method=2`

, estimates the fraction of pairs for
which `x1`

is concordant with `S`

but `x2`

is not.

For binary responses the function `improveProb`

provides several
assessments of whether one set of predicted probabilities is better
than another, using the methods describe in
Pencina et al (2007). This involves NRI and IDI to test for
whether predictions from model `x1`

are significantly different
from those obtained from predictions from model `x2`

. This is a
distinct improvement over comparing ROC areas, sensitivity, or
specificity.

- Keywords
- regression, nonparametric, survival

##### Usage

`rcorrp.cens(x1, x2, S, outx=FALSE, method=1)`improveProb(x1, x2, y)

# S3 method for improveProb
print(x, digits=3, conf.int=.95, …)

##### Arguments

- x1
first predictor (a probability, for

`improveProb`

)- x2
second predictor (a probability, for

`improveProb`

)- S
a possibly right-censored

`Surv`

object. If`S`

is a vector instead, it is converted to a`Surv`

object and it is assumed that no observations are censored.- outx
set to

`TRUE`

to exclude pairs tied on`x1`

or`x2`

from consideration- method
see above

- y
a binary 0/1 outcome variable

- x
the result from

`improveProb`

- digits
number of significant digits for use in printing the result of

`improveProb`

- conf.int
level for confidence limits

- …
unused

##### Details

If `x1`

,`x2`

represent predictions from models, these
functions assume either that you are using a separate sample from the
one used to build the model, or that the amount of overfitting in
`x1`

equals the amount of overfitting in `x2`

. An example
of the latter is giving both models equal opportunity to be complex so
that both models have the same number of effective degrees of freedom,
whether a predictor was included in the model or was screened out by a
variable selection scheme.

Note that in the first part of their paper, Pencina et al. presented measures that required binning the predicted probabilities. Those measures were then replaced with better continuous measures that are implementedhere.

##### Value

a vector of statistics for `rcorrp.cens`

, or a list with class
`improveProb`

of statistics for `improveProb`

:

number of cases

number of events

number of non-events

mean of pairwise differences in probabilities for those with events and a pairwise difference of \(\mbox{probabilities}>0\)

mean of pairwise differences in probabilities for those without events and a pairwise difference of \(\mbox{probabilities}>0\)

mean of pairwise differences in probabilities for those with events and a pairwise difference of \(\mbox{probabilities}>0\)

mean of pairwise differences in probabilities for those without events and a pairwise difference of \(\mbox{probabilities}>0\)

Net Reclassification Index = \((pup.ev-pdown.ev)-(pup.ne-pdown.ne)\)

standard error of NRI

Z score for NRI

Net Reclassification Index = \(pup.ev-pdown.ev\)

SE of NRI of events

Z score for NRI of events

Net Reclassification Index = \(pup.ne-pdown.ne\)

SE of NRI of non-events

Z score for NRI of non-events

improvement in sensitivity

improvement in specificity

Integrated Discrimination Index

SE of IDI

Z score of IDI

##### References

Pencina MJ, D'Agostino Sr RB, D'Agostino Jr RB, Vasan RS (2008): Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat in Med 27:157-172. DOI: 10.1002/sim.2929

Pencina MJ, D'Agostino Sr RB, D'Agostino Jr RB, Vasan RS: Rejoinder: Comments on Integrated discrimination and net reclassification improvements-Practical advice. Stat in Med 2007; DOI: 10.1002/sim.3106

Pencina MJ, D'Agostino RB, Steyerberg EW (2011): Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat in Med 30:11-21; DOI: 10.1002/sim.4085

##### See Also

##### Examples

```
# NOT RUN {
set.seed(1)
library(survival)
x1 <- rnorm(400)
x2 <- x1 + rnorm(400)
d.time <- rexp(400) + (x1 - min(x1))
cens <- runif(400,.5,2)
death <- d.time <= cens
d.time <- pmin(d.time, cens)
rcorrp.cens(x1, x2, Surv(d.time, death))
#rcorrp.cens(x1, x2, y) ## no censoring
set.seed(1)
x1 <- runif(1000)
x2 <- runif(1000)
y <- sample(0:1, 1000, TRUE)
rcorrp.cens(x1, x2, y)
improveProb(x1, x2, y)
# }
```

*Documentation reproduced from package Hmisc, version 4.0-3, License: GPL (>= 2)*