标题： 配对$2\times2$交叉设计的模式混合模型 显示英文标题

标题： A pattern mixture model for a paired $2\times2$ crossover design

摘要： 在进行配对的$2\times2$交叉设计时，每个受试者与另一个具有相似特征的受试者配对。 然后将该配对随机分配到两种处理的相同顺序中。 也就是说，两名受试者先接受第一种实验处理，然后他们交叉并接受另一种实验处理。 在国家心脏、肺和血液研究所的哮喘临床研究网络（ACRN）进行的β肾上腺素反应基因型（BARGE）研究中使用的一种配对$2\times2$交叉设计已被其他地方描述过。 当由此设计产生的数据是平衡且完整的——或者至少任何缺失的数据都是随机出现的——可以使用一般的线性混合效应模型方法来分析数据。 在本文中，我们提出一种基于模式混合模型的方法，用于分析在非可忽略方式下存在数据缺失的配对$2\times2$交叉设计所产生的数据。 由于其固有的科学兴趣，我们特别关注治疗与受试者类型交互项的估计。 最后，我们将本文中描述的模式混合模型方法应用于来自BARGE研究的数据。 显示英文摘要

摘要： When conducting a paired $2\times2$ crossover design, each subject is paired with another subject with similar characteristics. The pair is then randomized to the same sequence of two treatments. That is, the two subjects receive the first experimental treatment, and then they cross over and receive the other experimental treatment(s). The paired $2\times2$ crossover design that was used in the Beta Adrenergic Response by GEnotype (BARGE) Study conducted by the National Heart, Lung and Blood Institute's Asthma Clinical Research Network (ACRN) has been described elsewhere. When the data arising from such a design are balanced and complete -- or if at least any missingness that occurs is at random -- general linear mixed-effects model methods can be used to analyze the data. In this paper, we present a method based on a pattern-mixture model for analyzing the data arising from a paired $2\times2$ crossover design when some of the data are missing in a non-ignorable fashion. Because of its inherent scientific interest, we focus our particular attention on the estimation of the treatment-by-type of subject interaction term. Finally, we illustrate the pattern-mixture model methods described in this paper on the data arising from the BARGE study.