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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 large variety of model formulations, both mechanistic and regression-based, and also includes finite mixture models as a special case. We also propose a computationally efficient $ L_{2} $-based estimator as an alternative to maximum likelihood estimation, which substantially reduces computational cost, and we establish its consistency. Through a case study on malaria serology, we demonstrate how the flexibility of the novel framework enables joint analyses across all ages while accounting for changes in transmission patterns. We conclude by outlining extensions of the proposed modelling framework and its relevance to other omics applications.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxag008","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T13:04:36Z","timestamp":1773234276000},"source":"Crossref","is-referenced-by-count":0,"title":["A flexible class of latent variable models for the analysis of antibody response data"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0640-181X","authenticated-orcid":false,"given":"Emanuele","family":"Giorgi","sequence":"first","affiliation":[{"name":"Department of Applied Health Science, University of Birmingham , Birmingham, B15 2TT,","place":["United Kingdom"]},{"name":"Centre for Health Informatics, Computing and Statistics, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University , Lancaster, LA1 4AT,","place":["United 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