Longitudinal studies allow investigators to correlate changes in time-dependent exposures or biomarkers with subsequent health outcomes. The use of baseline or time-dependent markers to predict a subsequent change in clinical status such as transition to a diseased state requires the formulation of appropriate classification and prediction error concepts. Similarly, the evaluation of markers that could be used to guide treatment require specification of operating characteristics associated with use of the marker. The first part of this course will introduce predictive accuracy concepts that allow evaluation of time-dependent sensitivity and specificity for prognosis of a subsequent event time. We will overview options that are appropriate for both baseline markers and for longitudinal markers. Methods will be illustrated using examples from HIV and cancer research. The second part of this course will involve a technology workshop that will introduce the R packages (survivalROC, risksetROC, compriskROC) that are currently available for predictive accuracy of survival model. This segment will include hands-on training and demonstration of how to use these R packages for answering research questions. Several real-data examples for analysis will be provided and the instructors will discuss implementation and interpretation.