Transform-Both-Sides Nonlinear Mixed Effects Models for Multiple Flow Exhaled Nitric Oxide Data

• Activity Number: 70
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Sandrah P. Eckel*+ and Noa Molshatzki
• Companies: University of Southern California and University of Southern California
• Keywords: air pollution ; asthma ; biomarkers ; nonlinear regression ; mixed models ; transform both sides

Abstract

The analysis of biomarkers in exhaled breath is an important and emerging field. One such biomarker, the fractional concentration of exhaled nitric oxide (FeNO), is a marker of airway inflammation that is of growing interest for clinical and epidemiological applications (e.g., asthma management and air pollution health effects studies). FeNO level depends on flow rate. Mathematical models have been developed to describe FeNO as a function of flow and parameters representing airway and alveolar sources of NO. Measurements of FeNO at multiple flow rates for a given individual can be used to estimate these parameters using nonlinear regression. We have shown that applying a natural log transformation to both sides of the nonlinear regression improves model performance. Now, we investigate extensions of the Box-Cox transformation to nonlinear mixed effects models to estimate the optimal transformation of both sides. Multiple flow FeNO data is available in > 1500 children in the Southern California Children's Health Study (CHS). We apply the transform-both-sides method to CHS data.