Developing Biomarker Combinations in Multicenter Studies via Direct Maximization and Penalization

TL;DR

This paper introduces a novel method for developing biomarker combinations across multiple centers by directly maximizing the center-adjusted AUC and penalizing variability to ensure consistent performance, demonstrated through simulations and an acute kidney injury study.

Contribution

It proposes a new approach to biomarker combination development that directly maximizes a center-adjusted AUC and controls variability across centers, improving predictive performance and consistency.

Findings

Maximizing aAUC improves biomarker combination performance.

Penalizing variability yields more consistent results across centers.

Method shows promising results in simulations and real data.

Abstract

Motivated by a study of acute kidney injury, we consider the setting of biomarker studies involving patients at multiple centers where the goal is to develop a biomarker combination for diagnosis, prognosis, or screening. As biomarker studies become larger, this type of data structure will be encountered more frequently. In the presence of multiple centers, one way to assess the predictive capacity of a given combination is to consider the center-adjusted AUC (aAUC), a summary of the ability of the combination to discriminate between cases and controls in each center. Rather than using a general method, such as logistic regression, to construct the biomarker combination, we propose directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar performance across centers. To that end, we allow for penalization of the variability in the center-specific AUCs. We demonstrate desirable asymptotic properties of the resulting combinations. Simulations provide small-sample evidence that maximizing the aAUC can lead to combinations with improved performance. We also use simulated data to illustrate the utility of constructing combinations by maximizing the aAUC while penalizing variability. Finally, we apply these methods to data from the study of acute kidney injury.