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Activity Number: 529 - Contributed Poster Presentations: WNAR
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #306896
Title: A Semiparametric Approach to Modeling Nonlinear Longitudinal Drug Concentration Data Utilizing Standard Software
Author(s): Samantha MaWhinney* and Mary Morrow and Jose Castillo-Mancilla and Peter Anderson
Companies: University of Colorado Anschutz Medical Campus and Colorado School of Public Health and University of Colorado - Denver|Anschutz, Dept. of Medicine, Division of Infectious Diseas and University of Colorado, School of Pharmacy
Keywords: pharmacokinetic data; B-splines

Longitudinal drug concentration or pharmacokinetic data are often analyzed using nonlinear mixed models, which provide interpretable parameter estimates and concentration predictions. This compartmental approach relies on a biological framework for the concentration trajectory and allows for differing assessment times between subjects and/or sparse data. The disadvantage of this method is reliance on special software, difficulty in developing and validating the model and optimization issues. A simpler traditional approach is a two-stage, non-compartmental model. An outcome summary variable, such as area under the curve, is calculated which is subsequently analyzed. This approach can utilize standard software, but assumes similar sampling times and may not be appropriate for sparse data. We consider a different analysis approach when non-sparse, data are collected, but there is between-subject variability in measurement times. A semi-parametric model utilizing natural b-splines, which may be interacted with drug category, provides a smooth concentration trajectory and allows prediction within the data range utilizing a linear mixed model and standard software.

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

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