diagnostic: Diagnostic tests

In ThresholdROC: Optimum Threshold Estimation

Diagnostic tests

Description

This function calculates common measures of accuracy of diagnostic tests involving 2x2 contingency tables of classification results (usually, test outcome versus status tables).

Usage

diagnostic(tab, method = c("par", "exact"),
  casecontrol = FALSE, p = NULL, conf.level = 0.95)

Arguments

tab	an object of class table or matrix in the following form: TP FP FN TN where TP=number of true positives, FP=number of false positives, FN=number of false negatives and TN=number of true negatives; that is, a table where the first row corresponds to the positive tests and the second row to the negative tests; the first column corresponds to the diseased subjects and the second one to the healthy individuals. dim(tab) should be c(2, 2).
method	method for calculating the confidence intervals for sensitivity, specificity, predictive values, accuracy and error rate. The user can choose between "par" (parametric) and "exact" (exact). The last one is recommended for small sample sizes. Default, "par". The user can specify just the initial letters. See Details.
casecontrol	were data collected in a case-control study? Default, FALSE.
p	disease prevalence (only when casecontrol = TRUE; otherwise, this parameter is ignored).
conf.level	confidence level for the confidence intervals. Default, 0.95 (95%).

Details

For details about the expressions for the statistical measures calculated by this function, see References.

Since sensitivity, specificity, predictive values, accuracy and error rate are proportions, their confidence intervals are calculated using the functions prop.test (if method = "par") and binom.test (if method = "exact") from the stats package.

Confidence intervals for the likelihood ratios are calculated using the formulas proposed in Zhou et al (2002), Section 4.1.3. Furthermore, when likelihood ratios can not be calculated due to division by 0, the following correction is applied: 0.5 units are added to tab.

Confidence intervals for the odds ratio are calculated using the formulas proposed in Zhou et al (2002), Section 4.1.4. The same correction described before is applied when odds ratio can not be calculated due to division by 0.

Confidence intervals for the Youden index are calculated using the expression

CI(1-\alpha) = (Y-z_{1-\alpha/2}*Var(Y), Y+z_{1-\alpha/2}*Var(Y)),

where Y is the Youden index estimate, z_{1-\alpha/2} is the 1-\alpha/2 quantile of a N(0, 1) distribution and Var(Y) is the variance of the Youden index estimator, which is calculated as Var(Sensitivity)+Var(Specificity).

Value

A data.frame with ten rows and three columns containing the point estimate and confidence intervals for the following statistical measures: sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, odds ratio, Youden index, accuracy and error rate.

Author(s)

Sara Perez-Jaume, Natalia Pallares

References

Youden, WJ. (1950). Index for rating diagnostic tests. Cancer 3:32-35.

Zhou XH, Obuchowski NA and McClish DK. (2002). Statistical methods in diagnostic medicine. John Wiley and sons.

See Also

thres2

Examples

# example 1 (Zhou et al, 2002)
japan <- matrix(c(56, 23, 6, 78), ncol=2, byrow=TRUE)
colnames(japan) <- c("D", "nD")
rownames(japan) <- c("+", "-")
japan
p <- 0.196 # disease prevalence
diagnostic(japan, "par", casecontrol=TRUE, p=p)

# example 2
table <- matrix(c(22, 2, 3, 3), ncol=2, byrow=TRUE)
diagnostic(table, "par")
diagnostic(table, "exact")

# example 3
table2 <- matrix(c(22, 2, 0, 3), ncol=2, byrow=TRUE)
diagnostic(table2, "exact")

# example 4
# generate a random sample of diseased and non-diased subjects
n1 <- 100
n2 <- 100
set.seed(1234)
par1.1 <- 0
par1.2 <- 1
par2.1 <- 2
par2.2 <- 1
k1 <- rnorm(n1, par1.1, par1.2) # non-diseased
k2 <- rnorm(n2, par2.1, par2.2) # diseased
# threshold estimation
rho <- 0.2 # prevalence
thres <- thres2(k1, k2, rho, method="equal", ci.method="delta")$T$thres
# diagnostic test using the threshold estimate
marker <- c(k1, k2) # biomarker
condition <- c(rep("nD", length(k1)), rep("D", length(k2))) # condition
test <- ifelse(marker<thres, "-", "+") # test outcome according to thres
# build the table
table3 <- table(test, condition)[2:1, ]
# diagnostic test
round(diagnostic(table3), 3)

ThresholdROC documentation built on Aug. 30, 2023, 1:08 a.m.