Combining Biomarker Trajectories to Improve Diagnostic Accuracy in Prospective Cohort Studies with Verification Bias

• Activity Number: 470 - Biomarker Evaluation and Winning Student Papers on Medical Devices and Diagnostics
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Hong Li* and Constantine Gatsonis
• Companies: Medical University of South Carolina and Brown University
• Keywords: biomarker trajectory; functional data analysis; missing data mechanism; monitoring disease recurrence

Abstract

We develop methods to combine multiple biomarker trajectories into a composite diagnostic marker using functional data analysis (FDA) to achieve better diagnostic accuracy in monitoring disease recurrence in the setting of a prospective cohort study. In such studies the disease status is usually verified only for patients with a positive test result in any biomarker and is missing in patients with negative test results in all biomarkers. Thus the test result will affect disease verification, which leads to verification bias if the analysis is restricted only to the verified cases. We treat verification bias as a missing data problem. Under both missing at random (MAR) and missing not at random (MNAR) assumptions, we derive the optimal classification rules using the Neyman-Pearson lemma based on the composite diagnostic marker. We estimate thresholds adjusted for verification bias to dichotomize patients as test positive or test negative, and evaluate the diagnostic accuracy using the verification bias corrected area under ROC curves (AUC). We evaluate the performance and robustness of the FDA combination approach and assess the consistency of the approach through simulation studies