compute.threshold.pooledROC: Pooled ROC based threshold values.

Description

This function implements methods for estimating pooled ROC-based threshold values.

Usage

compute.threshold.pooledROC(object, criterion = c("FPF", "TPF", "YI"), FPF, TPF, 
  ci.level = 0.95, parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL)

Value

As a result, the function provides a list with the following components:

call: The matched call.
threshold: If method = "YI", the estimated (optimal) threshold corresponding to the Youden index (the one that maximises TPF/sensitivity + TNF/specificity). If method = "FPF", the estimated threshold corresponding to the specified FPF, and if method = "TPF", the estimated threshold corresponding to the specified TPF. For the Bayesian approaches (pooledROC.dpm and pooledROC.BB), and in both cases, in addition to the posterior mean, the ci.level*100% credible interval is also returned.
YI: If method = "YI", the estimated Youden index. For the Bayesian approaches (pooledROC.dpm and pooledROC.BB), in addition to the posterior mean, the ci.level*100% credible interval is also returned.
FPF: If method = "YI" or method = "TPF", the FPF corresponding to the estimated (optimal) threshold (For the Bayesian approaches (pooledROC.dpm and pooledROC.BB), in addition to the posterior mean, the ci.level*100% credible interval is also returned). If method = "FPF", the supplied FPF argument.
TPF: If method = "YI" or method = "FPF", the TPF/sensitivity corresponding to the estimated (optimal) threshold. For the Bayesian approaches (pooledROC.dpm and pooledROC.BB), in addition to the posterior mean, the ci.level*100% credible interval is also returned. If method = "TPF", the supplied TPF argument.

Arguments

object: An object of class pooledROC as produced by pooledROC.BB, pooledROC.emp, pooledROC.kernel, or pooledROC.dpm functions.
criterion: A character string indicating if the threshold value should be computed based on the Youden index (``YI''), or for fixed false positive fractions (``FPF'') or true positive fractions (``TPF'').
FPF: For criterion = "FPF", a numeric vector with the FPF at which to calculate the threshold values. Atomic values are also valid.
TPF: For criterion = "TPF", a numeric vector with the TPF at which to calculate the threshold values. Atomic values are also valid.
ci.level: An integer value (between 0 and 1) specifying the confidence level. The default is 0.95.
parallel: A characters string with the type of parallel operation: either "no" (default), "multicore" (not available on Windows) or "snow".
ncpus: An integer with the number of processes to be used in parallel operation. Defaults to 1.
cl: An object inheriting from class cluster (from the parallel package), specifying an optional parallel or snow cluster if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the call.

Details

Estimates pooled ROC-based threshold values based on three different criteria, namely, the Youden index (YI), one that gives rise to a pre-specified FPF, and one that gives rise to a pre-specified TPF.

The Youden Index is defined as $$YI = \max_{c}\{TPF(c) - FPF(c)\} = \max_{c}\{F_{\bar{D}}(c) - F_{D}(c)\},$$ where $$F_{D}(y) = Pr(Y_{D} \leq y),$$ $$F_{\bar{D}}(y) = Pr(Y_{\bar{D}} \leq y).$$ The value $c^{*}$ that achieves the maximum is called the optimal YI threshold. Regarding the criterion for a fixed FPF, the threshold value is obtained as follows $$c = F_{\bar{D}}^{-1}(1-FPF).$$ and for a fixed TPF we have $$c = F_{D}^{-1}(1-TPF).$$

References

Rutter, C.M. and Miglioretti, D. L. (2003). Estimating the Accuracy of Psychological Scales Using Longitudinal Data. Biostatistics, 4, 97--107.

Youden, W. J. (1ci.level*1000). Index for rating diagnostic tests. Cancer, 3, 32--35.

Examples

Run this code

library(ROCnReg)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]

# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
# \donttest{
m0_dpm <- pooledROC.dpm(marker = "l_marker1", group = "status",
            tag.h = 0, data = newpsa, standardise = TRUE, 
            p = seq(0,1,l=101), compute.WAIC = TRUE, compute.lpml = TRUE, 
            compute.DIC = TRUE, 
            prior.h = priorcontrol.dpm(m0 = 0, S0 = 10, a = 2, b = 0.5, alpha = 1, 
            L =10),
            prior.d = priorcontrol.dpm(m0 = 0, S0 = 10, a = 2, b = 0.5, alpha = 1, 
            L =10),
            mcmc = mcmccontrol(nsave = 400, nburn = 100, nskip = 1))


## Threshold values based on the YI
th_m0_dpm_yi <- compute.threshold.pooledROC(m0_dpm, criterion = "YI")

th_m0_dpm_yi$threshold
th_m0_dpm_yi$YI

### Threshold values for a fixed FPF
th_m0_dpm_fpf <- compute.threshold.pooledROC(m0_dpm, criterion = "FPF", FPF = 0.1)

th_m0_dpm_fpf$threshold
# }
# \dontshow{
m0_dpm <- pooledROC.dpm(marker = "l_marker1", group = "status",
            tag.h = 0, data = newpsa, standardise = TRUE,  
            p = seq(0,1,l=101), compute.WAIC = FALSE, compute.lpml = FALSE, 
            compute.DIC = FALSE, 
            prior.h = priorcontrol.dpm(m0 = 0, S0 = 10, a = 2, b = 0.5, alpha = 1, 
            L = 10),
            prior.d = priorcontrol.dpm(m0 = 0, S0 = 10, a = 2, b = 0.5, alpha = 1, 
            L = 10),
            mcmc = mcmccontrol(nsave = 40, nburn = 10, nskip = 1))

## Threshold values based on the YI
th_m0_dpm_yi <- compute.threshold.pooledROC(m0_dpm, criterion = "YI")

th_m0_dpm_yi$threshold
th_m0_dpm_yi$YI

### Threshold values for a fixed FPF
th_m0_dpm_fpf <- compute.threshold.pooledROC(m0_dpm, criterion = "FPF", FPF = 0.1)

th_m0_dpm_fpf$threshold
# }

Run the code above in your browser using DataLab