A Bayesian contamination model for serial dilution assays

Serial dilution assays are essential tools in biomedical research. Current analytical methods work well when measurements are in the steep part of the calibration curve but are not so efficient when measurements are near the upper and lower bounds. Moreover, sample contamination is common in serial dilution assays and can result in problematic concentration estimation. To address these challenges, we propose a Bayesian contamination model that simultaneously flags contaminated samples and estimates the concentrations of unknown samples, while quantifying estimation uncertainty. In extensive simulation studies under various contamination scenarios, our method consistently outperforms commonly used approaches. We further demonstrate a step-by-step application of a Bayesian workflow for modeling contamination using raw immunoassay data from the New York City Neighborhood Asthma and Allergy Study. This work advances previous research by introducing a practical method for detecting contaminated samples and estimating unknown concentrations in immunoassays and by presenting a general framework for incorporating unknown contamination processes into the Bayesian modeling workflow.