Optimal and efficient crossover designs when subject effects are random

• 成果类型: Article
• 署名作者: Hedayat, A. S.; Stufken, John; Yang, Min
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University System of Georgia; University of Georgia; University of Missouri System; University of Missouri Columbia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000001384
• 发表日期: 2006
• 页码: 1031-1038
• 关键词: universal optimality; MODEL; self

摘要

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.