Optimal Design Strategy to Achieve a Pre-Specified Power When the Biomarker Is Subject to Measurement Error

• Activity Number: 500
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Matthew Thomas White* and Sharon X. Xie
• Companies: Boston Children's Hospital and University of Pennsylvania
• Keywords: Biomarker ; Measurement error ; Power analysis ; Reliability sample ; Study design ; Diagnostic testing

Abstract

The discovery of effective diagnostic biomarkers is an important and highly active area of research. This discovery is often impeded, however, when biomarkers are obtained with measurement error, which may cause the biomarker to appear ineffective if not taken into account in the analysis. We develop an optimal design strategy to study the effectiveness of an error-prone biomarker in differentiating diseased from non-diseased individuals and focus on the area under the receiver operating characteristic curve (AUC) as the primary measure of effectiveness. Using an internal reliability sample within the diseased and non-diseased groups, we develop an optimal study design strategy that achieves a pre-specified power. We develop optimal allocations of the number of subjects, the size of the reliability sample, and the number of replicate observations per subject in the reliability sample within each group under a variety of commonly-seen study conditions and show that the pre-specified power is achieved through extensive simulations.