| Title: | Optimum Threshold Estimation |
|---|---|
| Description: | Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>. |
| Authors: | Sara Perez-Jaume [aut, cre], Natalia Pallares [aut], Konstantina Skaltsa [aut] |
| Maintainer: | Sara Perez-Jaume <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.9.4 |
| Built: | 2024-12-09 06:52:34 UTC |
| Source: | CRAN |
This dataset, obtained by Konstantina Skaltsa from Kapaki et al. 2003, contains measurements from the tau protein located in the cerebrospinal fluid (Tau variable) of 49 control subjects (Status==0) and 49 patients with Alzheimer's disease (Status==1). A column indicating the identifier of the subject is also included (id variable).
data("AD")data("AD")
Kapaki E, Paraskevas G, Zalonis I, Zournas C. (2003). CSF Tau Protein and beta-amyloid (1-42) in Alzheimer's Disease diagnosis: Discrimination from Normal Ageing and the Other Dementias in the Greek Population. European Journal of Neurology, 10, 119-128.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
This dataset contains Standardized Uptake Value (SUV) measurements (SUV variable) from 50 patients of breast cancer who underwent chemotherapy. After surgery, response to chemotherapy was evaluated using the pathology results in the surgical specimens, which was taken as gold standard by assigning one of the following three states: stable disease, partial remission and complete remission (resp variable). A column indicating the identifier of the patient is also included (id variable).
data("chemo")data("chemo")
Duch J, Fuster D, Munoz M, Fernandez P, Paredes P, Fontanillas M, Guzman F, Rubi S, Lomena F, Pons F. (2009). 18 F-FDG PET/CT for Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer. European Journal of Nuclear Molecular Imaging, 36, 1551-1557.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
This function estimates common accuracy measures for binary diagnostic tests involving 2x2 contingency tables of classification results (usually, test outcome versus status tables).
diagnostic(tab, method = c("par", "exact"), casecontrol = FALSE, p = NULL, conf.level = 0.95)diagnostic(tab, method = c("par", "exact"), casecontrol = FALSE, p = NULL, conf.level = 0.95)
tab |
an object of class 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. |
method |
method for calculating the confidence intervals for sensitivity, specificity, predictive values, accuracy and error rate. The user can choose between |
casecontrol |
were data collected in a case-control study? Default, |
p |
disease prevalence (only when |
conf.level |
confidence level for the confidence intervals. Default, 0.95 (95%). |
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
where is the Youden index estimate, is the quantile of a distribution and is the variance of the Youden index estimator, which is calculated as .
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.
Sara Perez-Jaume, Natalia Pallares
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.
# 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)# 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)
The function includes vertical lines for the threshold and confidence interval in a plot created with plot.thres2().
## S3 method for class 'thres2' lines(x, ci = TRUE, which.boot = c("norm", "perc"), col = 1, lty = c(1, 2), lwd = 1, ...)## S3 method for class 'thres2' lines(x, ci = TRUE, which.boot = c("norm", "perc"), col = 1, lty = c(1, 2), lwd = 1, ...)
x |
an object of class |
ci |
should the confidence interval be plotted? Default, |
which.boot |
in case |
col |
color for the threshold and its corresponding confidence interval. Default, 1. |
lty |
a 2-dimensional vector containing:
Default, |
lwd |
line width for the threshold and its corresponding confidence interval. Default, 1. |
... |
further arguments to be passed to |
With a plot.thres2 open, this function adds lines for the required threshold.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres <- thres2(k1, k2, rho, method="eq", ci.method="d") plot(thres, col=c(1, 2, 4), lwd=c(2, 2, 1), leg.pos="topright") thresunequal <- thres2(k1, k2, rho, method="unequal", ci=FALSE) lines(thresunequal, col=3) # almost the same; no confidence interval ## Not run: thresboot <- thres2(k1, k2, rho, method="emp", ci.method="b") lines(thresboot, col=5, which.boot="norm") ## End(Not run)n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres <- thres2(k1, k2, rho, method="eq", ci.method="d") plot(thres, col=c(1, 2, 4), lwd=c(2, 2, 1), leg.pos="topright") thresunequal <- thres2(k1, k2, rho, method="unequal", ci=FALSE) lines(thresunequal, col=3) # almost the same; no confidence interval ## Not run: thresboot <- thres2(k1, k2, rho, method="emp", ci.method="b") lines(thresboot, col=5, which.boot="norm") ## End(Not run)
The function includes vertical lines for the thresholds and confidence intervals in a plot created with plot.thres3().
## S3 method for class 'thres3' lines(x, ci = TRUE, which.boot = c("norm", "perc"), col = 1, lty = c(1, 2), lwd = 1, ...)## S3 method for class 'thres3' lines(x, ci = TRUE, which.boot = c("norm", "perc"), col = 1, lty = c(1, 2), lwd = 1, ...)
x |
an object of class |
ci |
should the confidence intervals be plotted? Default, |
which.boot |
in case |
col |
color for the thresholds and their corresponding confidence intervals. Default, 1. |
lty |
a 2-dimensional vector containing:
Default, |
lwd |
line width for the thresholds and their corresponding confidence intervals. Default, 1. |
... |
further arguments to be passed to |
With a plot.thres3 open, this function adds lines for the required threshold estimates.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres1 <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") # not assuming trinormality start2 <- c(0.05, 0.6, 0.5, 0.95) set.seed(2014) thres2 <- thres3(k1, k2, k3, rho, start=start2, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") plot(thres2, leg.pos="topright", leg.cex=0.8, col=1:4) lines(thres1, col=5)set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres1 <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") # not assuming trinormality start2 <- c(0.05, 0.6, 0.5, 0.95) set.seed(2014) thres2 <- thres3(k1, k2, k3, rho, start=start2, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") plot(thres2, leg.pos="topright", leg.cex=0.8, col=1:4) lines(thres1, col=5)
This function provides a graph including the sample densities (diseased and non-diseased populations), the threshold and its confidence interval.
## S3 method for class 'thres2' plot(x, bw = c("nrd0", "nrd0"), ci = TRUE, which.boot = c("norm", "perc"), col = c(1, 2, 3), lty = c(1, 1, 1, 2), lwd = c(1, 1, 1), legend = TRUE, leg.pos = "topleft", leg.cex = 1, xlim = NULL, ylim = NULL, main = paste0("Threshold estimate ", ifelse(ci, "and CI ", ""), "(method ", x$T$method, ")"), xlab = "", ...)## S3 method for class 'thres2' plot(x, bw = c("nrd0", "nrd0"), ci = TRUE, which.boot = c("norm", "perc"), col = c(1, 2, 3), lty = c(1, 1, 1, 2), lwd = c(1, 1, 1), legend = TRUE, leg.pos = "topleft", leg.cex = 1, xlim = NULL, ylim = NULL, main = paste0("Threshold estimate ", ifelse(ci, "and CI ", ""), "(method ", x$T$method, ")"), xlab = "", ...)
x |
an object of class |
bw |
vector containing the bandwith for the non-diseased sample in the first position and the bandwith for the diseased sample in the second position (to be passed to |
ci |
should the confidence interval be plotted? Default, |
which.boot |
in case |
col |
a 3-dimensional vector containing:
Default, |
lty |
a 4-dimensional vector containing:
Default, |
lwd |
a 3-dimensional vector containing:
Default, |
legend |
logical asking if an automatic legend should be added to the graph. Default, |
leg.pos |
position of the legend. Default, |
leg.cex |
number that reescales the size of the legend. Ignored if |
xlim |
2-dimensional vector indicating the lower and upper limits for x-axis. Default value (NULL) sets those limits automatically. |
ylim |
2-dimensional vector indicating the lower and upper limits for y-axis. Default value (NULL) sets those limits automatically. |
main, xlab, ...
|
further arguments to be passed to |
Estimates of the density functions for both samples and vertical lines representing the threshold and its confidence limits are drawn.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres <- thres2(k1, k2, rho, method="eq", ci.method="d") plot(thres, col=c(1, 2, 4), lwd=c(2, 2, 1), leg.pos="topright")n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres <- thres2(k1, k2, rho, method="eq", ci.method="d") plot(thres, col=c(1, 2, 4), lwd=c(2, 2, 1), leg.pos="topright")
This function provides a graph including the three sample densities, the thresholds and their confidence intervals.
## S3 method for class 'thres3' plot(x, bw = c("nrd0", "nrd0", "nrd0"), ci = TRUE, which.boot = c("norm", "perc"), col = c(1, 2, 3, 4), lty = c(1, 1, 1, 1, 2), lwd = c(1, 1, 1, 1), legend = TRUE, leg.pos = "topleft", leg.cex = 1, xlim = NULL, ylim = NULL, main = paste0("Threshold estimates", ifelse(ci, " and CIs", "")), xlab = "", ...)## S3 method for class 'thres3' plot(x, bw = c("nrd0", "nrd0", "nrd0"), ci = TRUE, which.boot = c("norm", "perc"), col = c(1, 2, 3, 4), lty = c(1, 1, 1, 1, 2), lwd = c(1, 1, 1, 1), legend = TRUE, leg.pos = "topleft", leg.cex = 1, xlim = NULL, ylim = NULL, main = paste0("Threshold estimates", ifelse(ci, " and CIs", "")), xlab = "", ...)
x |
an object of class |
bw |
vector containing the bandwith for the first sample in the first position, the bandwith for the second sample in the second position and the bandwith for the third sample in the third position (to be passed to |
ci |
should the confidence intervals be plotted? Default, |
which.boot |
in case |
col |
a 4-dimensional vector containing:
Default, |
lty |
a 5-dimensional vector containing:
Default, |
lwd |
a 4-dimensional vector containing:
Default, |
legend |
logical asking if an automatic legend should be added to the graph. Default, |
leg.pos |
position of the legend. Default, |
leg.cex |
number that reescales the size of the legend. Ignored if |
xlim |
2-dimensional vector indicating the lower and upper limits for x-axis. Default value (NULL) sets those limits automatically. |
ylim |
2-dimensional vector indicating the lower and upper limits for y-axis. Default value (NULL) sets those limits automatically. |
main, xlab, ...
|
further arguments to be passed to |
Estimates of the density functions for the three samples and vertical lines representing the thresholds and their confidence limits are drawn.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") plot(thres, leg.pos="topright") # not assuming trinormality thres <- thres3(k1, k2, k3, rho, dist1="lnorm", dist2="norm", dist3="norm", ci.method="boot") plot(thres, leg.pos="topright", which.boot="perc")set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") plot(thres, leg.pos="topright") # not assuming trinormality thres <- thres3(k1, k2, k3, rho, dist1="lnorm", dist2="norm", dist3="norm", ci.method="boot") plot(thres, leg.pos="topright", which.boot="perc")
This function plots the ROC curve and the cost function associated to the disease prevalence and costs.
plotCostROC(x, type = "l", which.plot, ...)plotCostROC(x, type = "l", which.plot, ...)
x |
an object of class |
type |
1-character string giving the type of plot desired. Default, |
which.plot |
which plot should be produced? The user can choose between |
... |
other graphical parameters to be passed to |
If the argument x was constructed with method="empirical", the argument extra.info should be switched to TRUE (this only applies when x is an object of class thres2).
For parameter type the following values are possible: "p" for points, "l" for lines, "b" for both points and lines, "c" for empty points joined by lines, "o" for overplotted points and lines, "s" and "S" for stair steps and "h" for histogram-like vertical lines. Finally, "n" does not produce any points or lines.
When x is an object of class thres2, two plots are produced. The first one shows the cost function with the cost minimising threshold in red. The second one is the step ROC curve with the sensitivity and specificity achieved in red. If method = "empirical" is used when building x, empirical cost function and ROC curve are plotted. If method = "smooth" is used when building x, smooth cost function and ROC curve are plotted.
When x is an object of class thres3, two plots are produced. The first one shows the cost function C(T1) with the first cost minimising threshold in red. The second one shows the cost function C(T2) with the second cost minimising threshold in red.
This function uses the plot() function and further arguments can be set to customise the resulting plot.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
## Not run: # example 1: x is an object of class 'thres2' n1 <- 100 n2 <- 100 set.seed(19998) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.3 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased x <- thres2(k1, k2, rho, method="emp", ci.method="boot", extra=TRUE) par(mfrow=c(1,2)) plotCostROC(x) # example 2: x is an object of class 'thres3' set.seed(2015) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) y <- thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") par(mfrow=c(1,2)) plotCostROC(y) ## End(Not run)## Not run: # example 1: x is an object of class 'thres2' n1 <- 100 n2 <- 100 set.seed(19998) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.3 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased x <- thres2(k1, k2, rho, method="emp", ci.method="boot", extra=TRUE) par(mfrow=c(1,2)) plotCostROC(x) # example 2: x is an object of class 'thres3' set.seed(2015) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) y <- thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") par(mfrow=c(1,2)) plotCostROC(y) ## End(Not run)
This function calculates the second partial derivative of the cost function at a given threshold.
secondDer2(x)secondDer2(x)
x |
an object of class |
This function evaluates the second derivative of the cost function at the threshold estimate so that the user can assess if this is positive (meaning that the estimation of the threshold leads to a minimum in the cost function) or close to zero and hence the minimum of the cost function does not exist. A closed formula is given when assuming binormality, otherwise the second derivative is evaluated numerically in the threshold estimate using functions from the package numDeriv.
The value of the second derivative of the cost function at the given threshold.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased x <- thres2(k1, k2, rho, method="equal", ci.method="delta") secondDer2(x)n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased x <- thres2(k1, k2, rho, method="equal", ci.method="delta") secondDer2(x)
This function calculates the second partial derivatives of the cost function at a given pair of thresholds.
secondDer3(x)secondDer3(x)
x |
an object of class |
This function evaluates the second partial derivatives of the cost function at the threshold estimates so that the user can assess if these are positive (meaning that the estimation of the thresholds leads to a minimum in the cost function) or close to zero and hence the minimum of the cost function does not exist. A closed formula is given when assuming trinormality, otherwise the second derivatives are evaluated numerically in the threshold estimates using functions from the package numDeriv.
The value of the second derivative of the cost function at the given thresholds.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) start <- c(mean(k1), mean(k3)) x <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") secondDer3(x)set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) start <- c(mean(k1), mean(k3)) x <- thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") secondDer3(x)
Estimates the sample size and the optimum sample size ratio needed for a given width, costs, disease prevalence and significance level under the assumption of binormality.
SS(par1.1, par1.2, par2.1, par2.2=NULL, rho, width, costs=matrix(c(0, 0, 1, (1-rho)/rho), 2, 2, byrow=TRUE), R=NULL, var.equal=FALSE, alpha=0.05)SS(par1.1, par1.2, par2.1, par2.2=NULL, rho, width, costs=matrix(c(0, 0, 1, (1-rho)/rho), 2, 2, byrow=TRUE), R=NULL, var.equal=FALSE, alpha=0.05)
par1.1 |
healthy population mean. |
par1.2 |
healthy population standard deviation. |
par2.1 |
diseased population mean. |
par2.2 |
diseased population standard deviation. It can be omitted when assuming equal variances (that is, when |
rho |
disease prevalence. |
width |
desired interval width. |
costs |
cost matrix. Costs should be entered as a 2x2 matrix, where the first row corresponds to the true positive and true negative costs and the second row to the false positive and false negative costs. Default cost values are a combination of costs that yields R=1, which is equivalent to the Youden index method (for details about this concept, see References). It must be set to |
R |
if the cost matrix |
var.equal |
a logical variable indicating whether to use equal variances. Default, |
alpha |
significance level for the confidence interval. Default, 0.05. |
an object of class SS which is a list with eight components:
ss2 |
sample size for the diseased group |
ss1 |
sample size for the healthy group |
epsilon |
sample size ratio between non-diseased and diseased subjects |
width |
width of the confidence interval provided by the user |
alpha |
significance level provided by the user |
costs |
cost matrix provided by the user |
R |
R term, the product of the non-disease odds and the cost ratio (for further details about this concept, see References) |
prev |
disease prevalence provided by the user |
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.3 width <- 0.5 SS(par1.1, par1.2, par2.1, par2.2, rho, width, var.equal=TRUE)par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.3 width <- 0.5 SS(par1.1, par1.2, par2.1, par2.2, rho, width, var.equal=TRUE)
This function calculates the threshold estimate and its corresponding confidence interval in a two-state setting.
thres2(k1, k2, rho, costs = matrix(c(0, 0, 1, (1 - rho)/rho), 2, 2, byrow = TRUE), R=NULL, method = c("equal", "unequal", "empirical", "smooth", "parametric"), dist1 = NULL, dist2 = NULL, ci = TRUE, ci.method = c("delta", "boot"), B = 1000, alpha = 0.05, extra.info = FALSE, na.rm = FALSE, q1=0.05, q2=0.95)thres2(k1, k2, rho, costs = matrix(c(0, 0, 1, (1 - rho)/rho), 2, 2, byrow = TRUE), R=NULL, method = c("equal", "unequal", "empirical", "smooth", "parametric"), dist1 = NULL, dist2 = NULL, ci = TRUE, ci.method = c("delta", "boot"), B = 1000, alpha = 0.05, extra.info = FALSE, na.rm = FALSE, q1=0.05, q2=0.95)
k1 |
vector containing the healthy sample values. |
k2 |
vector containing the diseased sample values. |
rho |
disease prevalence. |
costs |
cost matrix. Costs should be entered as a 2x2 matrix, where the first row corresponds to the true positive and true negative costs and the second row to the false positive and false negative costs. Default cost values are a combination of costs that yields R=1, which is equivalent to the Youden index method (for details about this concept, see References). It must be set to |
R |
if the cost matrix |
method |
method used in the estimation. The user can specify just the initial letters. Default, |
dist1 |
distribution to be assumed for the healthy population. See Details. |
dist2 |
distribution to be assumed for the diseased population. See Details. |
ci |
should a confidence interval be calculated? Default, |
ci.method |
method to be used for the confidence intervals calculation. The user can specify just the initial letters. Default, |
B |
number of bootstrap resamples when |
alpha |
significance level for the confidence interval. Default, 0.05. |
extra.info |
when using |
na.rm |
a logical value indicating whether |
q1 |
probability of the left distribution in order to determine a low quantile when |
q2 |
probability of the right distribution in order to determine a high quantile when |
For parameter method the user can choose between "equal" (assumes binormality and equal variances), "unequal" (assumes binormality and unequal variances), "empirical" (leaves out any distributional assumption), "smooth" (leaves out any distributional assumption, but uses a kernel to estimate the densities) or "parametric" (based on the distribution assumed for the two populations).
Parameters dist1 and dist2 can be chosen between the following 2-parameter distributions: "beta", "cauchy", "chisq" (chi-squared), "gamma", "lnorm" (lognormal), "logis" (logistic), "norm" (normal) and "weibull". Notice that dist1 and dist2 are only needed when method = "parametric".
For parameter ci.method the user can choose between "delta" (delta method is used to estimate the threshold standard error assuming a binormal underlying model) or "boot" (the confidence interval is calculated by bootstrap).
An object of class thres2, which is a list with two components:
T |
a list of at least seven components:
When
When
|
CI |
When
When
When |
It is assumed that k1 is the sample with lower values. If that is not the case, k1 and k2 (and the corresponding parameters) are exchanged.
Efron B, Tibshirani RJ. (1993). An introduction to the bootstrap, Chapman & Hall.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
thresTH2, plot.thres2, lines.thres2
# example 1 n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres2(k1, k2, rho, method="eq", ci.method="d") thres2(k1, k2, rho, method="uneq", ci.method="d") # specify R instead of (default) costs thres2(k1, k2, rho, costs=NULL, R=2, method="uneq", ci.method="d") ## Not run: thres2(k1, k2, rho, method="empirical", ci.method="b") # example 2 set.seed(1234) k1 <- rnorm(50, 10, 3) k2 <- rlnorm(55) rho <- 0.3 thres2(k1, k2, rho, method="param", ci.method="boot", dist1="norm", dist2="lnorm") ## End(Not run) # supress confidence intervals calculation thres2(k1, k2, rho, method="equal", ci=FALSE) thres2(k1, k2, rho, method="empirical", ci=FALSE) # example 3 n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased ## Not run: thres2(k1, k2, rho, method="smooth", ci.method="b") ## End(Not run)# example 1 n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased thres2(k1, k2, rho, method="eq", ci.method="d") thres2(k1, k2, rho, method="uneq", ci.method="d") # specify R instead of (default) costs thres2(k1, k2, rho, costs=NULL, R=2, method="uneq", ci.method="d") ## Not run: thres2(k1, k2, rho, method="empirical", ci.method="b") # example 2 set.seed(1234) k1 <- rnorm(50, 10, 3) k2 <- rlnorm(55) rho <- 0.3 thres2(k1, k2, rho, method="param", ci.method="boot", dist1="norm", dist2="lnorm") ## End(Not run) # supress confidence intervals calculation thres2(k1, k2, rho, method="equal", ci=FALSE) thres2(k1, k2, rho, method="empirical", ci=FALSE) # example 3 n1 <- 100 n2 <- 100 set.seed(1234) par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.2 k1 <- rnorm(n1, par1.1, par1.2) # non-diseased k2 <- rnorm(n2, par2.1, par2.2) # diseased ## Not run: thres2(k1, k2, rho, method="smooth", ci.method="b") ## End(Not run)
This function calculates the threshold estimates and their corresponding confidence intervals in a three-state setting.
thres3(k1, k2, k3, rho, costs = matrix(c(0, 1, 1, rho[1]/rho[2], 0, rho[3]/rho[2], 1, 1, 0), 3, 3, byrow = TRUE), dist1 = "norm", dist2 = "norm", dist3 = "norm", start = NULL, ci = TRUE, ci.method = c("param", "boot"), B = 1000, alpha = 0.05, na.rm = FALSE)thres3(k1, k2, k3, rho, costs = matrix(c(0, 1, 1, rho[1]/rho[2], 0, rho[3]/rho[2], 1, 1, 0), 3, 3, byrow = TRUE), dist1 = "norm", dist2 = "norm", dist3 = "norm", start = NULL, ci = TRUE, ci.method = c("param", "boot"), B = 1000, alpha = 0.05, na.rm = FALSE)
k1 |
vector containing the first sample values. |
k2 |
vector containing the second sample values. |
k3 |
vector containing the third sample values. |
rho |
3-dimensional vector of prevalences. |
costs |
cost matrix. Costs should be entered as a 3x3 matrix, where the first row corresponds to the costs associated with the classification of subjects in state 1 (C11, C12 and C13), second row corresponds to the costs associated with the classification of subjects in state 2 (C21, C22 and C23) and the third row corresponds to the costs associated with classification of subjects in state 3 (C31, C32, C33), where Cij is the cost of classifying an individual of class i as class j. Default cost values are a combination of costs that leads to the same thresholds as the Youden index method (see References for details). |
dist1 |
distribution to be assumed for the first population. Default, |
dist2 |
distribution to be assumed for the second population. Default, |
dist3 |
distribution to be assumed for the third population. Default, |
start |
when the three distributions |
ci |
should a confidence interval be calculated? Default, |
ci.method |
method to be used for the confidence intervals calculation. The user can specify just the initial letters. Default, |
B |
number of bootstrap resamples when |
alpha |
significance level for the confidence interval. Default, 0.05. |
na.rm |
a logical value indicating whether |
Parameters dist1, dist2 and dist3 can be chosen between the following 2-parameter distributions: "beta", "cauchy", "chisq" (chi-squared), "gamma", "lnorm" (lognormal), "logis" (logistic), "norm" (normal) and "weibull".
For parameter ci.method the user can choose between "param" (parametric confidence intervals are calculated when assuming a trinormal underlying model) and "boot" (the confidence intervals are calculated by bootstrap).
When at least one of the distributions is not "norm", the function internally uses the thresTH3() function, which requires two intervals in which the two thresholds are expected to be found. These intervals are determined by the default values of thresTH3(). When all the distributions are "norm", the function uses the nlm() function, which requires two starting values (passed through the argument start).
An object of class thres3, which is a list with two components:
T |
a list of at least ten components:
When not all distributions are normal,
|
CI |
When
When
When |
It is assumed that k1 is the sample with lower values and k3 is the one taking higher values. If that is not the case, k1, k2 and k3 (and the corresponding parameters) are re-ordered as needed.
Efron B, Tibshirani RJ. (1993). An introduction to the bootstrap, Chapman & Hall.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
thresTH3, plot.thres3, lines.thres3
set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") # not assuming trinormality thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") # supress confidence intervals calculation thres3(k1, k2, k3, rho, ci=FALSE, dist1="lnorm", dist2="norm", dist3="norm")set.seed(1234) n <- 100 k1 <- rlnorm(n) k2 <- rnorm(n, 3, 1) k3 <- rnorm(n, 5, 1) rho <- c(1/3, 1/3, 1/3) # assuming trinormality start <- c(mean(k1), mean(k3)) thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param") # not assuming trinormality thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm") # supress confidence intervals calculation thres3(k1, k2, k3, rho, ci=FALSE, dist1="lnorm", dist2="norm", dist3="norm")
The ThresholdROC package provides point and interval estimations of the optimum threshold as well as graphical tools for continuous diagnostic tests (two- and three- state settings). The point estimation is based on the definition of a cost function which we opt to minimise. An analytical estimator is available for the binormal and trinormal model and the empirical one is used for all settings. The interval estimation is based on the Delta method variance estimator in a binormal parametric setting and on methods on non-linear equations for the trinormal setting. Bootstrap methods are also provided for the confidence intervals.
The most important functions are thres2 and thres3. They offer a wide range of options for threshold estimation and inference in two and three state settings. We also include the function diagnostic, which calculates common measures of accuracy for binary diagnostic tests involving 2x2 contingency tables of classification results.
Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Maintainer: Sara Perez-Jaume <[email protected]>
Efron B, Tibshirani RJ. (1993). An introduction to the bootstrap, Chapman & Hall.
Perez-Jaume S, Skaltsa K, Pallares N, Carrasco JL. (2017). ThresholdROC: Optimum Threshold Estimation Tools for Continuous Diagnostic Tests in R. Journal of Statistical Software 82(4):1-21. doi: 10.18637/jss.v082.i04.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
Zhou XH, Obuchowski NA and McClish DK. (2002). Statistical methods in diagnostic medicine. John Wiley and sons.
This function estimates the theoretical optimum threshold for the specific distribution parameters, decision costs and disease prevalence in a two-state setting.
thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho, costs = matrix(c(0, 0, 1, (1 - rho)/rho), 2, 2, byrow = TRUE), R=NULL, q1 = 0.05, q2 = 0.95, tol = 10^(-8))thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho, costs = matrix(c(0, 0, 1, (1 - rho)/rho), 2, 2, byrow = TRUE), R=NULL, q1 = 0.05, q2 = 0.95, tol = 10^(-8))
dist1 |
distribution to be assumed for the healthy population. See Details. |
dist2 |
distribution to be assumed for the diseased population. See Details. |
par1.1 |
first parameter of the distribution chosen for the healthy population. |
par1.2 |
second parameter of the distribution chosen for the healthy population. |
par2.1 |
first parameter of the distribution chosen for the diseased population. |
par2.2 |
second parameter of the distribution chosen for the diseased population. |
rho |
disease prevalence. |
costs |
cost matrix. Costs should be entered as a 2x2 matrix, where the first row corresponds to the true positive and true negative costs and the second row to the false positive and false negative costs. Default cost values are a combination of costs that yields R=1, which is equivalent to the Youden index method (for details about this concept, see References). It must be set to |
R |
if the cost matrix |
q1 |
probability of the left distribution in order to determine a low quantile. Default, 0.05. |
q2 |
probability of the right distribution in order to determine a high quantile. Default, 0.95. |
tol |
tolerance to be used in function |
Parameters dist1 and dist2 can be chosen between the following 2-parameter distributions: "beta", "cauchy", "chisq" (chi-squared), "gamma", "lnorm" (lognormal), "logis" (logistic), "norm" (normal) and "weibull".
An object of class thresTH2, which is a list with five components:
thres |
threshold estimate. |
prev |
disease prevalence provided by the user. |
costs |
cost matrix provided by the user. |
R |
R term, the product of the non-disease odds and the cost ratio (for further details about this concept, see References). |
method |
method used in the estimation. For an object of class |
It is assumed that dist1 is the distribution with lower values. If not, dist1 and dist2 (and the corresponding parameters) are exchanged.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
# example 1 dist1 <- "norm" dist2 <- "norm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.1 thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho) # example 2 dist1 <- "norm" dist2 <- "lnorm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 1 par2.2 <- 0.5 rho <- 0.3 thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho)# example 1 dist1 <- "norm" dist2 <- "norm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 rho <- 0.1 thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho) # example 2 dist1 <- "norm" dist2 <- "lnorm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 1 par2.2 <- 0.5 rho <- 0.3 thresTH2(dist1, dist2, par1.1, par1.2, par2.1, par2.2, rho)
This function estimates the theoretical optimum thresholds for the specific distribution parameters, decision costs and prevalences in a three-state setting.
thresTH3(dist1, dist2, dist3, par1.1, par1.2, par2.1, par2.2, par3.1, par3.2, rho, costs = matrix(c(0, 1, 1, rho[1]/rho[2], 0, rho[3]/rho[2], 1, 1, 0), 3, 3, byrow = TRUE), q1=0.05, q2=0.5, q3=0.95, tol = 10^(-8))thresTH3(dist1, dist2, dist3, par1.1, par1.2, par2.1, par2.2, par3.1, par3.2, rho, costs = matrix(c(0, 1, 1, rho[1]/rho[2], 0, rho[3]/rho[2], 1, 1, 0), 3, 3, byrow = TRUE), q1=0.05, q2=0.5, q3=0.95, tol = 10^(-8))
dist1 |
distribution to be assumed for the first population. See Details. |
dist2 |
distribution to be assumed for the second population. See Details. |
dist3 |
distribution to be assumed for the third population. See Details. |
par1.1 |
first parameter of the first distribution. |
par1.2 |
second parameter of the first distribution. |
par2.1 |
first parameter of the second distribution. |
par2.2 |
second parameter of the second distribution. |
par3.1 |
first parameter of the third distribution. |
par3.2 |
second parameter of the third distribution. |
rho |
3-dimensional vector of prevalences. |
costs |
cost matrix. Costs should be entered as a 3x3 matrix, where the first row corresponds to the costs associated with the classification of subjects in state 1 (C11, C12 and C13), second row corresponds to the costs associated with the classification of subjects in state 2 (C21, C22 and C23) and the third row corresponds to the costs associated with classification of subjects in state 3 (C31, C32, C33), where Cij is the cost of classifying an individual of class i as class j. Default cost values are a combination of costs that leads to the same thresholds as the Youden index method (see References for details). |
q1 |
probability of the distribution taking lower values in order to determine a low quantile. Default, 0.05. See Details. |
q2 |
probability of the middle distribution in order to determine a medium quantile. Default, 0.5. See Details. |
q3 |
probability of the the distribution taking higher values in order to determine a high quantile. Default, 0.95. See Details. |
tol |
tolerance to be used in function |
Parameters dist1, dist2 and dist3 can be chosen between the following 2-parameter distributions: "beta", "cauchy", "chisq" (chi-squared), "gamma", "lnorm" (lognormal), "logis" (logistic), "norm" (normal) and "weibull".
Parameters q1, q2 and q3 are used to determine two intervals where the uniroot function should look for the two threshold estimates. Thus, the first threshold is expected to be found between quantile-1(q1) and quantile-2(q2) and the second one, between quantile-2(q2) and quantile-3(q3), being quantile-i() the quantile function for the i-th distribution, i=1,2,3.
An object of class thresTH3, which is a list with five components:
thres1 |
first threshold estimate. |
thres2 |
second threshold estimate. |
prev |
prevalences provided by the user. |
costs |
cost matrix provided by the user. |
method |
method used in the estimation. For an object of class |
It is assumed that dist1 is the distribution with lower values and dist3 is the one taking higher values. If that is not the case, dist1, dist2 and dist3 (and the corresponding parameters) are re-ordered as needed.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
# example 1 dist <- "norm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 par3.1 <- 4 par3.2 <- 1 rho <- c(1/3, 1/3, 1/3) thresTH3(dist, dist, dist, par2.1, par2.2, par1.1, par1.2, par3.1, par3.2, rho) # example 2 dist1 <- "norm" dist2 <- "lnorm" dist3 <- "lnorm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 1 par2.2 <- 0.5 par3.1 <- 2 par3.2 <- 0.5 rho <- rep(1/3, 3) thresTH3(dist1, dist2, dist3, par1.1, par1.2, par2.1, par2.2, par3.1, par3.2, rho)# example 1 dist <- "norm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 2 par2.2 <- 1 par3.1 <- 4 par3.2 <- 1 rho <- c(1/3, 1/3, 1/3) thresTH3(dist, dist, dist, par2.1, par2.2, par1.1, par1.2, par3.1, par3.2, rho) # example 2 dist1 <- "norm" dist2 <- "lnorm" dist3 <- "lnorm" par1.1 <- 0 par1.2 <- 1 par2.1 <- 1 par2.2 <- 0.5 par3.1 <- 2 par3.2 <- 0.5 rho <- rep(1/3, 3) thresTH3(dist1, dist2, dist3, par1.1, par1.2, par2.1, par2.2, par3.1, par3.2, rho)