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Comparing sampling methods for pharmacokinetic studies using model averaged derived parameters
Author(s) -
Barnett Helen Yvette,
Geys Helena,
Jacobs Tom,
Jaki Thomas
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7436
Subject(s) - pharmacokinetics , equivalence (formal languages) , sampling (signal processing) , plasma concentration , computer science , statistics , mathematics , pharmacology , medicine , filter (signal processing) , discrete mathematics , computer vision
Pharmacokinetic studies aim to study how a compound is absorbed, distributed, metabolised, and excreted. The concentration of the compound in the blood or plasma is measured at different time points after administration and pharmacokinetic parameters such as the area under the curve ( A U C ) or maximum concentration ( C m a x ) are derived from the resulting concentration time profile. In this paper, we want to compare different methods for collecting concentration measurements (traditional sampling versus microsampling) on the basis of these derived parameters. We adjust and evaluate an existing method for testing superiority of multiple derived parameters that accounts for model uncertainty. We subsequently extend the approach to allow testing for equivalence. We motivate the methods through an illustrative example and evaluate the performance using simulations. The extensions show promising results for application to the desired setting.