survAccuracyMeasures: Estimate the AUC, TPR(c), FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff value c using non-parametric and semi-parametric methods. Bootstrap standard errors and confidence intervals are also computed.
survCompetingRisk: Evaluate the prognostic accuracy of a marker with multiple competing risk events. Functions to calculate the AUC, ROC, PPV, and NPV are provided. A discrete covariate Z, if available, can be included.
survNRI: Calculate the net reclassification index (NRI) statistic for survival data using five different estimators.
partlyconditional Provides functions to fit partly conditional models, a class of models useful for clinical contexts where long term follow-up information is available on a patient population, along with multiple measures of patient health and other biological markers collected across time. Interest lies in modeling a patients’ future risk of an adverse event, given survival to time s, as a function of longitudinal marker history recorded up to time s.
longsurvMarkerTwoPhase: Functions to evaluate the prognostic accuracy of a partly conditional model fit using a longitudinal marker from a case-cohort (CCH) or a nested case-control (NCC) sample, using methods described in "Evaluating longitudinal markers under two-phase study designs’" by Marlena Maziarz, Tianxi Ci, Li Qi, Anna Lok and Yingye Zheng.
survMarkerTwoPhase: Evaluate the prognostic accuracy of a marker under two phase designs (case-cohort and nested case control designs).
AIPWmeasures: Estimate measures of predictive accuracy using augmented inverse probability weights for two-phase biomarker validation studies.
Shiny web applications to simulate power for prognostic biomarker validation studies with time to event data.