A flexible class of latent variable models for the analysis of antibody response data

Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into two distinct groups: seronegative and seropositive. Here, we challenge this dichotomous modelling assumption and propose a latent variable modelling framework in which the immune status of each individual is represented along a continuum of latent seroreactivity, ranging from minimal to strong immune activation. This formulation provides greater flexibility in capturing age-related changes in antibody distributions while preserving the full information content of quantitative measurements. We show that the proposed class of models can accommodate a great variety of model formulations, both mechanistic and regression-based, and also includes finite mixture models as a special case. We demonstrate the advantages of this approach using malaria serology data and its ability to develop joint analyses across all ages that account for changes in transmission patterns. We conclude by outlining extensions of the proposed modelling framework and its relevance to other omics applications.