Joe Koopmeiners
The development of diagnostic biomarkers for disease screening is a several phase process. In phase II, performance of the biomarker is compared to established levels of performance to determine if further study is warranted in larger Phase III studies. It is expected that most biomarkers identified in phase I will have inadequate performance. This, combined with the limited availability of samples for rare diseases, make group sequential designs that allow for early termination due to futility an attractive option for Phase II biomarker studies. An option for early termination will preserve specimens when a marker has inadequate performance but will lead to biased estimates at study completion for studies that do not terminate early. Estimates from studies that do not terminate early will be used to determine if the marker moves on in development and to design future studies. Therefore, we desire estimates that are unbiased conditional on not terminating early. We present a general framework for conditional estimation after a two-stage group sequential trial that allows for early termination. Three bias-corrected conditional estimators are proposed along with conditional confidence intervals. Estimators and confidence intervals are used to estimate ROC(t) after a two-stage group sequential trial that allows for early termination and simulation results are presented to evaluate their performance. Finally, use of these conditional estimators is illustrated by estimating ROC(t) for DCP, a new biomarker for liver cancer, after a two-stage trial that allows for early termination.
