A0571
Title: Comparing and combining data from immune assays with different limits of detection
Authors: Ying Huang - Fred Hutchinson Cancer Research Center (United States) [presenting]
Abstract: In vaccine research towards the prevention of infectious diseases, immune response biomarkers serve as an important tool for comparing and ranking vaccine candidates based on their immunogenicity and predicted protective effect. However, analyses of immune response outcomes can be complicated by differences across assays when immune response data are acquired from multiple groups/laboratories. Motivated by a real-world problem to accommodate the use of two different neutralization assays in COVID-19 vaccine trials, we propose methods that integrate external paired-sample data with bridging assumptions to achieve two objectives, both using pooled data acquired from different assays: i) comparing immunogenicity between vaccine regimens, and ii) evaluating correlates of risk. Our methods adjust for differences between assays with respect to measurement error and the lower limit of detection. Simulation studies were conducted to demonstrate the satisfactory performance of the proposed methods and their advantage over alternative approaches. We apply the proposed methods to SARS-CoV-2 spike-pseudotyped virus neutralization assay data generated in vaccine and convalescent samples by two different laboratories.
