Sequential Testing for Assessing the Incremental Value of Biomarkers Under Biorepository Specimen Constraints with Robustness to Model Misspecification

TL;DR

This paper introduces a robust two-stage sequential testing method for evaluating the incremental value of biomarkers in cancer detection, especially under specimen constraints and model misspecification, demonstrated through simulations and real data.

Contribution

It proposes a novel two-stage group sequential hypothesis testing framework with robustness to model misspecification for biomarker evaluation under limited samples.

Findings

Validates type I error control through simulations

Efficiently identifies promising biomarkers in real data

Supports multiple biomarker validation across labs

Abstract

In cancer biomarker development, a key objective is to evaluate whether a new biomarker, when combined with an established one, improves early cancer detection compared to using the established biomarker alone. Incremental value is often quantified by changes at specific points on the ROC curve, such as an increase in sensitivity at a fixed specificity, which is especially relevant in early cancer detection. Our research is motivated by the Early Detection Research Network (EDRN) biorepository studies, which aim to validate multiple cancer biomarkers across laboratories using specimens obtained from a centralized biorepository, under the constraint of limited specimen availability. To address this challenge, we propose a two-stage group sequential hypothesis testing framework for assessing incremental effects, allowing early stopping for futility or efficacy to conserve valuable samples. Our asymptotic results are derived under a logistic working model and remain valid even under model misspecification, ensuring robustness and broad applicability. We further integrate a rotating group membership design to facilitate validation of multiple candidate biomarkers across laboratories. Through extensive simulations, we demonstrate valid type I error control and efficient utilization of specimens. Finally, we apply our method to data from a multicenter EDRN pancreatic cancer reference set study and show how the proposed approach identifies promising candidate biomarkers that provide incremental performance when combined with CA19-9, while enabling efficient evaluation of a large number of such candidates.