Mori Joji, Kano Yutaka
Division of Mathematical Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, Japan.
Pharm Stat. 2015 Nov-Dec;14(6):464-70. doi: 10.1002/pst.1703. Epub 2015 Jul 27.
The crossover trial design (AB/BA design) is often used to compare the effects of two treatments in medical science because it performs within-subject comparisons, which increase the precision of a treatment effect (i.e., a between-treatment difference). However, the AB/BA design cannot be applied in the presence of carryover effects and/or treatments-by-period interaction. In such cases, Balaam's design is a more suitable choice. Unlike the AB/BA design, Balaam's design inflates the variance of an estimate of the treatment effect, thereby reducing the statistical power of tests. This is a serious drawback of the design. Although the variance of parameter estimators in Balaam's design has been extensively studied, the estimators of the treatment effect to improve the inference have received little attention. If the estimate of the treatment effect is obtained by solving the mixed model equations, the AA and BB sequences are excluded from the estimation process. In this study, we develop a new estimator of the treatment effect and a new test statistic using the estimator. The aim is to improve the statistical inference in Balaam's design. Simulation studies indicate that the type I error of the proposed test is well controlled, and that the test is more powerful and has more suitable characteristics than other existing tests when interactions are substantial. The proposed test is also applied to analyze a real dataset.
交叉试验设计（AB/BA设计）在医学科学中常用于比较两种治疗方法的效果，因为它进行的是受试者内比较，这提高了治疗效果（即治疗组间差异）的精度。然而，在存在残留效应和/或治疗×时期交互作用的情况下，AB/BA设计无法应用。在这种情况下，巴拉姆设计是更合适的选择。与AB/BA设计不同，巴拉姆设计会使治疗效果估计值的方差膨胀，从而降低检验的统计功效。这是该设计的一个严重缺点。尽管对巴拉姆设计中参数估计量的方差已进行了广泛研究，但用于改进推断的治疗效果估计量却很少受到关注。如果通过求解混合模型方程获得治疗效果的估计值，则AA和BB序列会被排除在估计过程之外。在本研究中，我们开发了一种新的治疗效果估计量以及使用该估计量的新检验统计量。目的是改进巴拉姆设计中的统计推断。模拟研究表明，所提出检验的I型错误得到了很好的控制，并且当交互作用显著时，该检验比其他现有检验更具功效且具有更合适的特性。所提出的检验也被应用于分析一个真实数据集。
