Parameter Estimation Using Unidentified Individual Data in Individual Based Models

Authored by H T Banks, R Baraldi, J Catenacci, N Myers

Date Published: 2016

DOI: 10.1051/mmnp/201611602

Sponsors: U.S. Air Force Office of Scientific Research United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

In physiological experiments, it is common for measurements to be collected from multiple subjects. Often it is the case that a subject cannot be measured or identified at multiple time points (referred to as unidentified individual data in this work but often referred to as aggregate population data {[}5, Chapter 5]). Due to a lack of alternative methods, this form of data is typically treated as if it is collected from a single individual. This assumption leads to an overconfidence in model parameter values and model based predictions. We propose a novel method which accounts for inter-individual variability in experiments where only unidentified individual data is available. Both parametric and nonparametric methods for estimating the distribution of parameters which vary among individuals are developed. These methods are illustrated using both simulated data, and data taken from a physiological experiment. Taking the approach outlined in this paper results in more accurate quantification of the uncertainty attributed to inter-individual variability.

Tags

Uncertainty Differential-equations Variability Pharmacokinetic models