@article{9dc40a0dfb9341ca9cb2c216c1fee867,
title = "Optimal and efficient crossover designs when subject effects are random",
abstract = "Most studies on optimal crossover designs are based on models that assume subject effects to be fixed effects. In this article we identify and study optimal and efficient designs for a model with random subject effects. With the number of periods not exceeding the number of treatments, we find that totally balanced designs are universally optimal for treatment effects in a large subclass of competing designs. However, in the entire class of designs, totally balanced designs are in general not optimal, and their efficiency depends on the ratio of the subject effects variance and the error variance. We develop tools to study the efficiency of totally balanced designs and to identify designs with higher efficiency.",
keywords = "Fisher information matrix, Mixed-effects model, Totally balanced design, Universal optimality",
author = "Hedayat, {A. S.} and John Stufken and Min Yang",
note = "Funding Information: A. S. Hedayat is Distinguished Professor of Statistics, Department of Mathematics, Statistics, and Computer Science, University of Illinois, Chicago, IL 60607 (E-mail: hedayat@uic.edu). John Stufken is Professor of Statistics, Department of Statistics, University of Georgia, Athens, GA 30602 (E-mail: jstufken@stat.uga.edu). Min Yang is Assistant Professor, Department of Statistics, University of Missouri, Columbia, MO 65211 (E-mail: yangmi@missouri.edu). Hedayat{\textquoteright}s research was primarily sponsored by National Science Foundation (NSF) grant DMS-01-03727, National Cancer Institute grant P01-CA48112-08, and National Institutes of Health (NIH) grant P50-AT00155 ( jointly supported by the National Center for Complementary and Alternative Medicine, the Office of Dietary Supplements, the Office for Research on Women{\textquoteright}s Health, and the National Institute of General Medicine). Yang{\textquoteright}s research was supported by NSF grant DMS-03-04661. The contents are solely the responsibility of the authors and do not necessarily represent the official views of NSF and NIH. The authors are thankful for detailed comments and suggestions by the editor, an associate editor, and three referees on an earlier version of the article, which clearly helped to improve the final version.",
year = "2006",
month = sep,
doi = "10.1198/016214505000001384",
language = "English (US)",
volume = "101",
pages = "1031--1038",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "475",
}