Four-parameter paired response curve for serial dilution assays

Area of Science:

• Biomarker discovery and immunoassay development.
• Statistical modeling in biological and medical research.

Background:

• Advanced immunoassay platforms provide superior signal-to-noise ratios but incur higher costs.
• Performing assays across a full series of sample dilutions is often cost-prohibitive.

Purpose of the Study:

• To develop a cost-efficient method for immunoassay data analysis.
• To enable cross-protocol comparison of immune response biomarkers using limited sample dilutions.

Main Methods:

• A novel four-parameter paired response curve was developed to model the relationship between assay outcomes from two sample dilutions.
• Likelihood-based inference was employed for model fitting and analysis.
• The model predicts assay outcomes for new sample dilutions.

Main Results:

• The proposed paired response curve effectively models the relationship between assay results from different dilutions.
• The method allows for accurate prediction of assay outcomes for de novo dilutions.
• Numerical studies using simulated and real data demonstrated the model's utility.

Conclusions:

• The paired response curve offers a cost-effective approach to immunoassay data analysis.
• This method facilitates the comparison of immune response biomarkers across different assay protocols and dilution schemes.
• The approach enhances the utility of advanced immunoassay platforms by optimizing resource allocation.