Longitudinal Monitoring of Antibiotics with Antimicrobial Susceptibility Testing

• Activity Number: 331 - ASA Biopharmaceutical Section Student Paper Award Competition
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Will Eagan* and Bruce A. Craig
• Companies: Vertex Pharmaceuticals and Purdue University
• Keywords: Antimicrobial Resistance; Prevalence Estimation; Measurement Error; Bayesian; Antibiotics; Longitudinal Data

Abstract

Antimicrobial resistance (AMR) is a major challenge to modern medicine and of grave concern to public health. To monitor AMR cross-sectionally, researchers analyze “drug/bug” collections of clinical assay results to estimate AMR prevalence and the distribution of susceptible (wild-type) strains. This estimation is challenging because (a) the collection of assay results is a mixture of susceptible and resistant (non-wild-type) strains and (b) the most commonly used dilution assay produces interval-censored readings. To account for measurement error and utilize the full data set of bin counts, a novel a Bayesian semiparametric method is proposed. Similar to the previous mixture model methods, the wild-type distribution is modelled parametrically. Because less is known about the non-wild-type distribution, the proposed method uses a Dirichlet Process mixture model for the non-wild-type distribution. To accommodate longitudinal studies for the purpose of AMR monitoring, the subset methods and the proposed method are extended. The feasibility of this approach and its improved precision and accuracy are demonstrated through simulation studies and an application to a real data set.