Hierarchical Bayesian Models to Estimate the Effects of Determinants of Airway and Alveolar Nitric Oxide

• Activity Number: 297 - SPEED: Food, Environment, Biomedical Imaging and Physical System Visualization/Learning, Part 1
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jingying Weng* and Noa Molshatski and Paul Marjoram and Patrick Muchmore and Shujing Xu and Frank D Gilliland and Sandrah P Eckel
• Companies: and University of Southern California and University of Southern California and University of Southern California and University of Southern California and University of Southern California and University of Southern California
• Keywords: Air pollution; Asthma; FeNO; Gibbs Sampling; MCMC; Multilevel models

Abstract

Exhaled breath biomarkers are an important and emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations have been developed to describe FeNO measured at the mouth as a function of exhalation flow rate and so-called NO parameters representing proximal and distal sources of NO in the airway (CawNO, and CANO, respectively). Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to potential determinants (Stage II). We have developed a one-step, unified estimation approach with the goal of properly propagating uncertainty. We implement our Hierarchical Bayesian Model using the R interface to the JAGS (Just another Gibbs sampler) program. In simulations studies, we evaluated the statistical properties of our new model and compared the performance to that of a two-step frequentist approach. We will apply our method to the southern California Children’s Health Study to relate air pollution exposures to proximal and distal airway inflammation.