A geometric characterization of c-optimal designs for regression models with correlated observations

Loading...
Thumbnail Image

Date

2009-07-28

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

We consider the problem of optimal design of experiments for random effects models, especially population models, where a small number of correlated observations can be taken on each individual, while the observations corresponding to different individuals can be assumed to be uncorrelated. We focus on c-optimal design problems and show that the classical equivalence theorem and the famous geometric characterization of Elfving (1952) from the case of uncorrelated data can be adapted to the problem of selecting optimal sets of observations for the n individual patients. The theory is demonstrated in a linear model with correlated observations and a nonlinear random effects population model, which is commonly used in pharmacokinetics.

Description

Table of contents

Keywords

c-optimal design, correlated observations, Elfving's theorem, geometric characterization, locally optimal design, mixed models, pharmacokinetic models, random effects

Citation