Developing Performance-Guaranteed Biomarker Combination Rules with Integrated External Information under Practical Constraint

Music City Center 

In clinical practice, there is significant interest in integrating novel biomarkers with existing clinical data to construct interpretable and robust decision rules. Motivated by the need to improve decision-making for early disease detection, we propose a framework for developing an optimal biomarker-based clinical decision rule that is both clinically meaningful and practically feasible. Specifically, our procedure constructs a linear decision rule designed to achieve optimal performance among class of linear rules by maximizing the true positive rate while adhering to a pre-specified positive predictive value constraint. Additionally, our method can adaptively incorporate individual risk information from external source to enhance performance when such information is beneficial. We establish the asymptotic properties of our propose estimator and compare to the standard approach used in practice through extensive simulation studies. Results indicate that our approach demonstrates strong finite-sample performance. Finally, we apply the proposed methods to develop biomarker-based screening rules for pancreatic ductal adenocarcinoma (PDAC) among new-onset diabetes (NOD) patients.

Early detection

Cancer screening

Positive predictive value

Sensitivity