JSM 2004 - Toronto

Abstract #300502

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Activity Number: 404
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #300502
Title: Dealing with Model Bias in a Nonlinear Mixed Model
Author(s): Karen E.A. Chiswell*+ and John Monahan
Companies: North Carolina State University and North Carolina State University
Address: 2204 Hwy.158 W, Oxford, NC, 27565,
Keywords: first-order linearization ; nonlinear mixed model ; model misspecification ; PBPK model ; sensitivity analysis
Abstract:

A common assumption when fitting a nonlinear model is that the nonlinear function correctly specifies the mean trajectory being described. However, it is quite common in particular applications (e.g., toxicology, pharmacokinetics) to work with mechanistic models that suffer from some degree of misspecification, or model bias. An important implication of this bias, especially in the case where data have a longitudinal structure, is the resulting difficulty in modeling within-individual covariance structure. We examine various approaches to handling the apparent model bias in a physiologically based pharmacokinetic model (PBPK), used to analyze data from a closed chamber study of CCl4 metabolism in rats. These approaches include sensitivity analysis, first-order linearization (FOL) of the likelihood function (based on normality assumptions), and smoothing methods.

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