Time-dependent Predictive Values of Prognostic Biomarkers with Failure Time Outcome

In a prospective cohort study, information on clinical parameters, tests and molecular markers is often collected. Such information is useful to predict patient prognosis and to select patients for targeted therapy. We propose a new graphical approach, the positive predictive value (PPV) curve, to quantify the predictive accuracy of prognostic markers measured on a continuous scale with censored failure time outcome. The proposed method highlights the need to consider both predictive values and the marker distribution in the population when evaluating a marker, and it provides a common scale for comparing different markers. We consider both semiparametric and nonparametric based estimating procedures. In addition, we provide asymptotic distribution theory and resampling based procedures for making statistical inference. We illustrate our approach with numerical studies and datasets from the Seattle Heart Failure Study.