UNIVERSALLY OPTIMAL CROSSOVER DESIGNS UNDER SUBJECT DROPOUT

• 成果类型: Article
• 署名作者: Zheng, Wei
• 署名单位: Indiana University System; Indiana University Indianapolis
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1074
• 发表日期: 2013
• 页码: 63-90
• 关键词: model

摘要

Subject dropout is very common in practical applications of crossover designs. However, there is very limited design literature taking this into account. Optimality results have not yet been well established due to the complexity of the problem. This paper establishes feasible, as well as necessary and sufficient conditions for a crossover design to be universally optimal in approximate design theory in the presence of subject dropout. These conditions are essentially linear equations with respect to proportions of all possible treatment sequences being applied to subjects and hence they can be easily solved. A general algorithm is proposed to derive exact designs which are shown to be efficient and robust.