R packages for prospective biomarker validation with time-to-event data developed in collaboration with Yingye Zheng and Tianxi Cai.

### R packages

**Biomarker evaluation with survival data**

- Accuracy summary measures:
**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.* - Competing risk outcome:
**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.* - Incremental value evaluation:
**survNRI***Calculate the net reclassification index (NRI) statistic for survival data using five different estimators.*

**Prediction with longitudinal biomarkers**

- Longitudinal Prediction:
**partlyconditional***This package provides functions to fit partly conditional (PC) risk models, which are a helpful class of predictive models often used in medical contexts where long-term follow-up is available and interest lies in predicting patients' risks for a future adverse outcome using repeatedly measured predictors over time.* - LongitudinalAccuracy: longsurvAccuracyMeasures
*In development* - Two phase design with longitudinal data: longsurvMarkerTwoPhase
*In development*

**Two phase biomarker study design**

- Two phase design with survival data:
**survMarkerTwoPhase***Evaluate the prognostic accuracy of a marker under two phase designs (case-cohort and nested case control designs).* - Two phase design with survival data (efficient and flexible estimation): AIPWmeasure
**AIPWmeasures***Estimate measures of predictive accuracy using augmented inverse probability weights for two-phase biomarker validation studies.*

### Power Calculations

Shiny web applications to simulate power for prognostic biomarker validation studies with time to event data.