Improving the analysis and use of contaminated immunoassays: from methods development to implementation

PROJECT SUMMARY Immunoassays – tests that estimate the concentration of analytes in a sample using antibody-antigen binding reactions – are widely used for clinical diagnostics, biopharmaceutical analysis, and environmental monitoring. The current method for estimating concentrations of analytes measured in immunoassays has several limitations, including large measurement errors, difficulty in estimating very low or high concentrations, and noisy estimation. In addition, with the advance of new technologies using multiplex assays, multiple analytes can be measured simultaneously in a single plate, but the current method fails to account for the potential correlations between multiple analytes of the same sample. Further, operator error may arise and environmental samples can be contaminated, resulting in assay errors. Although some Bayesian methods exist that use immunoassay data more efficiently, they have not yet been incorporated into a practical workflow, in part because of concerns about robustness to model error. Motivated by these challenges, the proposed research aims to develop a new Bayesian workflow for the analysis of immunoassay data, considering possible error and contamination in the samples and providing a step-by-step guide to model building, checking, and validation. Further, to enhance the proper use of immunoassay data, a joint modeling and a two-step model approach will be developed to analyze exposure- outcome associations accounting for the uncertainty in the exposure measure by immunoassays. The methods will be developed and validated using immunoassay data of indoor allergens with dust samples from the New York City Neighborhood Asthma and Allergy Study. A lab protocol and a graphical user interface will be developed and tested to facilitate the uptake of the proposed methods. Once completed, the proposed research will provide methods and tools for the analysis and use of potential contaminated immunoassay data, advance research in the broader scientific field when dealing with model contamination and uncertainty in predictors, and provide important insights into allergic sensitization and asthma morbidity among asthmatic children.