BayesACME: A Bayesian Semiparametric Approach to Estimating a Bacterium’s Wild-Type Distribution and Prevalence: Accounting for Contamination and Measurement Error

• Activity Number: 231 - SPEED: SPAAC SESSION I
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Will A Eagan* and Bruce A. Craig
• Companies: Regeneron Pharmaceuticals and Purdue University
• Keywords: Antimicrobial Resistance; Measurement Error; Bayesian Analysis; Mixture Models; Semiparametric Methods; Dirichlet Process Mixture Model

Abstract

Antimicrobial resistance (AMR) is a major challenge to modern medicine and of grave concern to public health. To monitor AMR, researchers analyze “drug/bug" collections of clinical assay results to estimate both the 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. Despite current methods addressing the previous two challenges, they ignore the inherent assay variability. This variability can encompass a three-fold dilution range. To account for measurement error, we propose a Bayesian semiparametric method to handle both single-year and multiyear studies. The proposed method models the wild-type distribution parametrically and utilizes a Dirichlet Process mixture model for the non-wild-type distribution. Simulation studies and an application to a real data set demonstrate the feasibility of this approach and along with its improved precision and accuracy to the other methods.