Description Usage Arguments Value Note Author(s) References See Also Examples
This function computes a bootstrap confidence interval for the ROC curve at a given value false negative fraction (1  specificity) t. The ROC curve estimate is based on logconcave densities, as discussed in Rufibach (2011).
1 2 
cases 
Values of the continuous variable for the cases. 
controls 
Values of the continuous variable for the controls. 
grid 
Values of 1  specificity where confidence intervals should be computed at (may be a vector). 
conf.level 
Confidence level of confidence interval. 
M 
Number of bootstrap replicates. 
smooth 

output 

A list containing the following elements:
qs 

boot.mat 
Bootstrap samples for the ROC curve based on the logconcave density estimate. 
qs.smooth 
If 
boot.mat.smooth 
If 
The confidence intervals are only valid if observations are independent, i.e. eacht patient only contributes one measurement, e.g.
Kaspar Rufibach (maintainer)
kaspar.rufibach@gmail.com
http://www.kasparrufibach.ch.
The reference for computation of these bootstrap confidence intervals is:
Rufibach, K. (2012). A smooth ROC curve estimator based on logconcave density estimates. Int. J. Biostat., 8(1), 1–29.
The bootstrap competitor based on the empirical ROC curve is described in:
Zhou, X.H. and Qin, G. (2005). Improved confidence intervals for the sensitivity at a fixed level of specificity of a continuousscale diagnostic test. Statist. Med., 24, 465–477.
The ROC curve based on logconcave density estimates can be computed using logConROC
. In the example below we analyze the pancreas
data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Not run:
## ROC curve for pancreas data
data(pancreas)
status < factor(pancreas[, "status"], levels = 0:1, labels = c("healthy", "diseased"))
var < log(pancreas[, "ca199"])
cases < var[status == "diseased"]
controls < var[status == "healthy"]
## compute confidence intervals
res < confIntBootLogConROC_t0(controls, cases, grid = c(0.2, 0.8), conf.level = 0.95,
M = 1000, smooth = TRUE, output = TRUE)
res
## End(Not run)

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