Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2016
Committee Chair or Co-Chairs
Dr Michele Joyner
Committee Members
Dr Jeff Knisley, Dr Edith Seier
Abstract
Aggregate models incorporate the variation between individual parameters of individualbased models to construct a population-based model. This thesis focuses on the comparison of two different methods for creating these population-based models. The first method, the individual parameter distribution technique (IPD) focuses on the similarities and variation of parameters in an individual-based model as calculated using individual data sets [4]. The second method we consider is the nonlinear mixed effect method (NLME), which is primarily used in modeling repeated measurement data. In the NLME approach, both the fixed effects and random effects of the parameter values are estimated in the model by assuming a normal distribution for the parameter values across individuals[2]. Both methods were implemented on a one-compartment pharmacokinetic concentration model. Using the variation in parameters estimated using the two different approaches, a population model was generated and then compared to the dynamics seen in the individual data sets. We compare three features of the concentration data to the simulated population models. The values for all three features were captured by both methods; however, the biggest difference observed is 2 that there is a longer tail in the distribution for the population model developed using NLME than observed in the dynamics in the original data.
Document Type
Thesis - unrestricted
Recommended Citation
Oyero, Oyebola E., "Comparison of Two Methods for Developing Aggregate Population-Based Models" (2016). Electronic Theses and Dissertations. Paper 3139. https://dc.etsu.edu/etd/3139
Copyright
Oyebola Oyero