Campus Access Only
All rights reserved. This publication is intended for use solely by faculty, students, and staff of University of the Pacific. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, now known or later developed, including but not limited to photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author or the publisher.
Date of Award
Thesis - Pacific Access Restricted
Master of Science (M.S.)
First Committee Member
Second Committee Member
It has been recommended by the FDA and others that the population pharmacokinetic models (PPKM) need to be validated. This is particularly true when the model plays a key role in the construction of dosing strategies. It was the objective of the current study to evaluate the ability of bootstrapping to identify PPKMs that were estimated from data without influence observations versus PPKMs from data containing influence observations. The evaluation was performed in four phases. In phase I, ten no-influence index datasets and ten influence index datasets were created. A model parameter (theta !) was estimated for the index datasets. It was found that influence observations caused an over-estimation of the model parameter. In phase II, 200 bootstrap datasets were resampled with replacement from each of the twenty index datasets ( 4000 datasets total).
In phase III, the bootstrapping validation method was executed using NONMEM for model estimation and the resulting statistics were used to detect models developed from influence data. The metrics of choice were mean absolute prediction error (MAPE) and mean squared prediction error (MSPE). In phase IV, the impact of achieving a global minimum in the NONMEM program on the non-parametric bootstrap validation process was investigated. This study showed that the current and widely followed procedure for application of the bootstrap for PPK model validation has significant deficiencies. The achievement of a global minimum in the NONMEM program proved to be an important and pivotal factor when applying bootstrapping to the PPKM validation process. Therefore, we concluded that each bootstrap dataset should be evaluated with several model control streams. Further, the suggested value for an acceptable difference between the NONMEM minimum objective function values for a global and a near global minimum should be 2.5 units.
Al-otoum, Mohammed Fawzi. (2004). Evaluation of bootstrapping as a validation technique for population pharmacokinetic models. University of the Pacific, Thesis - Pacific Access Restricted. https://scholarlycommons.pacific.edu/uop_etds/590
To access this thesis/dissertation you must have a valid pacific.edu email address and log-in to Scholarly Commons.Find in PacificSearch
If you are the author and would like to grant permission to make your work openly accessible, please email