Antimicrobial resistance (AMR) has already been identified as an urgent issue affecting global health. Many research interests lie in monitoring the change in AMR in both human and animal populations. Moreover, it is important to study the correlation in AMR between the two populations. In this study, we develop a hierarchical latent class mixture model for the detection of linear changes and correlation analysis across populations with antimicrobial resistance. We propose Bayesian methods to estimate the unknown parameters in the proposed model. The simulation study is conducted to evaluate the empirical performance of the proposed method. Finally, we employ the proposed model and methodology to analyze the datasets obtained from the National Antimicrobial Resistance Monitoring System (NARMS).

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To cite this article: Jingheng Cai & Wei Zeng (28 Feb 2026): A hierarchical Bayesian latent class mixture model with censorship for detection of linear changes and correlation analysis across populations in antimicrobial resistance, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2026.2634423
To link to this article: https://doi.org/10.1080/24754269.2026.2634423