Key result
A new outcomes model for estimating antibody titers outperformed the traditional response approach regardless of data variability in a simulation study.
A newly developed outcomes model for estimating antibody titers in microneutralization assays outperforms traditional methods, particularly when handling irregular data.
Does not support changing assay practices; leaves open real-world superiority pending empirical validation.
Viruses that express reporter genes upon infection have been recently used to evaluate neutralizing antibody responses, where a lack of reporter expression indicates specific virus inhibition. The traditional model-based methods using standard outcome of percent neutralization could be applied to the data from the assays to estimate antibody titers. However, the data produced are sometimes irregular, which can yield meaningless outcomes of percent neutralization that do not fit the typical curves for immunoassays, making automated or semi-high throughput antibody titer estimation unreliable. We developed a type of new outcomes model, which is biologically meaningful and fits typical immunoassay curves well. Our simulation study indicates that the new response approach outperforms the traditional response approach regardless of the data variability. The proposed new response approach can be used in similar assays for other disease models.
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Yang et al. (2015) studied this question. New outcomes model for estimating antibody titers vs. Traditional model-based methods was evaluated on Antibody titer estimation performance. A new outcomes model for estimating antibody titers outperformed the traditional response approach regardless of data variability in a simulation study.
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