Two-stage adaptive cutoff design for building and validating a prognostic biomarker signature.

Abstract

Cancer biomarkers are frequently evaluated using archived specimens collected from previously conducted therapeutic trials. Routine collection and banking of high quality specimens is an expensive and time-consuming process. Therefore, care should be taken to preserve these precious resources. Here, we propose a novel two-stage adaptive cutoff design that affords the possibility to stop the biomarker study early if an evaluation of the model performance is unsatisfactory at an early stage, thereby allowing one to preserve the remaining specimens for future research. In addition, our design integrates important elements necessary to meet statistical rigor and practical demands for developing and validating a prognostic biomarker signature, including maintaining strict separation between the datasets used to build and evaluate the model and producing a locked-down signature to facilitate future validation. We conduct simulation studies to evaluate the operating characteristics of the proposed design. We show that under the null hypothesis when the model performance is deemed undesirable, the proposed design maintains type I error at the nominal level, has high probabilities of terminating the study early, and results in substantial savings in specimens. Under the alternative hypothesis, power is generally high when the total sample size and the targeted degree of improvement in prediction accuracy are reasonably large. We illustrate the use of the procedure with a dataset in patients with diffuse large-B-cell lymphoma. The practical aspects of the proposed designs are discussed. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.