Title: Compute and Compare Diagnostic Test Statistics Across Groups
Authors@R: c(person("Shenghai", "Dai", role = c("aut", "cre"), email = "s.dai@wsu.edu"),
person("Olasunkanmi J.", "Kehinde", role = "aut"),
person("Maureen", "Schmitter-Edgecombe", role = "aut"),
person("Brian F.", "French", role = "aut"))
Description: Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.1002/1097-0142%281950%293%3A1%3C32%3A%3AAID-CNCR2820030106%3E3.0.CO%3B2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.
Depends: R (>= 3.5.0), reportROC, pROC, stats

Title: Time-dependent ROC curve estimation and adaptation to the
Author: Y. Foucher, K. Trebern-Launay and M. Lorent
Description: This package contains functions in order to estimate time-dependent ROC with right-censored data. More precisely, two functions concern the estimation of ROC curves defined by Heagerty, Lumley and Pepe (Biometrics, 2000). The two other functions concern their adaptation for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population (same age, gender and calendar year). This last part is based on additive relative survival models and the work of Pohar-Perme (Biometrics, 2011).
Depends: R (>= 2.10), splines, date, survival, relsurv

Title: Time-Dependent ROC Curve Estimators and Expected Utility
Author: Y. Foucher, E. Dantan, P. Tessier, F. Le Borgne, and M. Lorent
Description: Contains functions in order to estimate diagnostic and prognostic capacities of continuous markers. More precisely, one function concerns the estimation of time-dependent ROC (ROCt) curve, as proposed by Heagerty et al. (2000) <DOI: 10.1111/j.0006-341X.2000.00337.x>. One function concerns the adaptation of the ROCt theory for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population. This last part is based on additive relative survival models and the work of Pohar-Perme et al. (2012) <DOI: 10.1111/j.1541-0420.2011.01640.x>. We also propose two functions for cut-off estimation in medical decision making by maximizing time-dependent expected utility function. Finally, we propose confounder-adjusted estimators of ROC and ROCt curves by using the Inverse Probability Weighting (IPW) approach.  For the confounder-adjusted ROC curve (without censoring), we also proposed the implementation of the estimator based on placement values proposed by Pepe and Cai (2004) <DOI: 10.1111/j.0006-341X.2004.00200.x>.
Depends: R (>= 2.10), splines, date, survival, relsurv, timereg