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Abstract:
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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. Oftentimes the analyses with immune response outcomes, however, are complicated by the difference across assays when immune response data are acquired from multiple laboratories. Motivated by a real-world problem to accommodate the use of two different neutralization assays in COVID-19 trials, we propose methods that combine paired-samples with bridging assumptions to achieve two objectives: i) comparing immunogenicity between vaccine regimens, and ii) evaluating correlates of risk using pooled data acquired from different assays. Our methods adjust for differences between assays with respect to measure error and the lower limit of detection. Simulation studies were conducted to demonstrate 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.
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