intcensROC (version 0.1.1)

intcensROC: Compute the ROC curves for Interval Censored Survival Data

Description

A method to compute the receiver operating characteristic (ROC) curve for the interval censored survival data based on a spline function based constrained maximum likelihood estimator. The maximization process of likelihood is carried out by generalized gradient projection method.

Usage

intcensROC(U, V, Marker, Delta, PredictTime, gridNumber = 500)

Arguments

U

An array contains left end time points of the observation time range for the interval censored data.

V

An array contains right end time points of the observation time range for the interval censored data.

Marker

An array contains marker levels for the samples.

Delta

An array of indicator for the censored type, use 1, 2, 3 for event happened before the left bound time, within the defined time range, and after.

PredictTime

A scalar indicates the predict time.

gridNumber

A integer for the number of gird for the ROC curve, the default value is 500.

Value

A dataframe with two columes

tp

A array for true positive rate for different marker levels in the range of 0 to 1.

fp

A array for false positive rate for different marker levels in the range of 0 to 1.

References

Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>

Examples

# NOT RUN {
## example data
U <- runif(100, min = 0.1, max = 5)
V <- runif(100, min = 0.1, max = 5) + U
Marker <- runif(100, min = 5, max = 10)
Delta  <- sample.int(3, size = 100, replace = TRUE)
pTime <- 4

## compute the ROC curve
res <- intcensROC(U, V, Marker, Delta, pTime, gridNumber = 500)
head(res)
# }