interleukin6: Interleukin 6 (IL6) data

In nsROC: Non-Standard ROC Curve Analysis

Description

This dataset includes the true-positives (TP), false-positives (FP), true-negatives (TN) and false-negatives (FN) reported by 9 different papers which study the use of the Interleukin 6 (IL6) as a marker for the early detection of neonatal sepsis.

Usage

interleukin6

Format

A data frame with 19 observations of the following 5 variables.

Author

a vector assigning different numbers to each paper

TP

vector of true positives

FP

vector of false positives

FN

vector of false negatives

TN

vector of true negatives

Details

In those papers providing more than one pair of Sensitivity-Specificity all of them are collected.

References

Martinez-Camblor P., 2017, Fully non-parametric receiver operating characteristic curve estimation for random-effects meta-analysis, Statistical Methods in Medical Research, 26(1), 5-20.

Examples

# Load the dataset
data(interleukin6)

# Plot pairs (FPR, TPR) for each Author

attach(interleukin6)

TPR <- TP/(TP+FN)
FPR <- FP/(FP+TN)
plot(FPR, TPR, xlim=c(0,1), ylim=c(0,1), lwd=10, pch=1, col='gray', xlab="False-Positive Rate",
    ylab="True-Positive Rate", main=paste("ROC curve interpolation"))

S <- unique(Author)
ind <- order(Author, FPR, TPR)
ord.data <- cbind(Author[ind], FPR[ind], TPR[ind])
roc.j <- sapply(S, function(j){
  lines(c(0,ord.data[Author==j,2],1), c(0,ord.data[Author==j,3],1), col='gray')})
for(i in 1:19){text(ord.data[i,2],ord.data[i,3],ord.data[i,1],cex=0.5)}

Example output

nsROC documentation built on May 2, 2019, 2:31 p.m.