rcorrp.cens {Hmisc}R Documentation

Rank Correlation for Paired Predictors with a Possibly Censored Response

Description

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

Usage

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

improveProb(x1, x2, y)

## S3 method for class '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 T 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.

Value

a vector of statistics (a list of statistics for improveProb)

Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

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.

See Also

rcorr.cens, somers2, Surv, val.prob

Examples

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)

[Package Hmisc version 3.7-0 Index]