We then discuss how our results can be combined with standard group sequential methods to develop group sequential testing procedures and bias-adjusted estimators for the PPV and NPV curve. First, we develop asymptotic theory for the sequential empirical PPV and NPV curves when the prevalence must be estimated, rather than assumed known in a case-control study. In this paper, we consider group sequential testing of the predictive accuracy of a continuous biomarker with unknown prevalence. A limitation of this approach is that the prevalence cannot be estimated from a case-control study and must be assumed known. Previously, we derived the asymptotic properties of the sequential empirical positive predictive value (PPV) and negative predictive value (NPV) curves, which summarize the predictive accuracy of a continuous marker, under case-control sampling.

The small sample properties of the proposed group sequential testing procedures and estimators are evaluated by simulation, and we illustrate our approach in the context of a study to validate a novel biomarker for prostate cancer.Ībstract = 'Group sequential testing procedures have been proposed as an approach to conserving resources in biomarker validation studies. Group sequential testing procedures have been proposed as an approach to conserving resources in biomarker validation studies.