Optimum threshold estimation based on cost function in a two and
three state setting
The ThresholdROC package provides point and interval estimations of the optimum threshold for continuous diagnostic tests (two and three states setting). The point estimation is based on the definition of a cost function which we opt to minimise. An analytical estimator exists 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.

Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Point and interval estimations of optimum thresholds for continuous diagnostic tests (two- and three- state settings).

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. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.

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>.

Threshold and AUC Estimation with Right-Censored Data at a Fixed
Time t
c(person("Sara", "Perez-Jaume", email="spjaume@gmail.com", role=c("aut", "cre")),
person(c("Josep", "L"), "Carrasco", role="aut"))
We focus on the estimation of optimal thresholds and AUCs when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is missing for those subjects censored before t. Here we provide three methods (missing exclusion, imputation of censored times and using time-dependent ROC curves) to estimate optimal thresholds and AUCs in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
boot, InformativeCensoring, pROC, psych, survival,
survivalROC, ThresholdROC
Sara Perez-Jaume [aut, cre],
Josep L Carrasco [aut]