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Activity Number: 540 - SPEED: Clinical Trial Design, Longitudinal Analysis, and Other Topics in Biopharmaceutical Statistics
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 11:35 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #332792
Title: Estimation of Peak Expiratory Flow Under Stochastic Differential Equations
Author(s): Shan Yang*
Companies: Merck & Co Inc
Keywords: Forced Expiratory Volume; Bayesian Regression; Stochastic Differential Equations

Rhinovirus (RV) infection is the most important trigger for asthma exacerbation. In the effort of standardizing GMP grade human RV challenge platform, estimation of the forced expiratory volume in one second (FEV1) is the airflow metric of primary interest. Single doses of 100 TCID50 were administered in asthmatics on ICS not taking LABA (n=23) and Days 1-7 TWA (time weighted average) morning and evening FEV1 were reduced by -1.7% and - 2.1% respectively under Bayesian Regression. In this paper, we explore and discuss the estimation of FEV1 deduction post rhinovirus challenge under stochastic differential equations (SDE) using both clinical data and simulated data.

Authors who are presenting talks have a * after their name.

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