Crossover designs robust to changes in carryover models are useful in clinical trials where the nature of carryover effects is not known in advance. The designs have been characterized for being optimal and efficient under no carryover-, traditional-, and, self and mixed carryover- models, however, ignoring the number of subjects, which has significant impact on both optimality and administrative convenience. In this article, adding two more practical models, the traditional, and, self and mixed carryover models having carryover effect only for the new or test treatment, a 5M algorithm is presented. The 5M algorithm based computer code searches all possible two treatment crossover designs under the five carryover models and list those which are optimal and /or efficient to all the five carryover models. The resultant exhaustive list consists of optimal and/or efficient crossover designs in two, three, and four periods, having 4 to 20 subjects of which 24 designs are new optimal for one of the established carryover models, and 34 designs are optimal for newly added models.

中文翻译：

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五个残留模型的最佳和/或高效两种处理交叉设计。

对残留模型的变化具有鲁棒性的交叉设计在临床研究中很有用，在这种临床试验中，残留效应的性质尚不清楚。该设计的特点是在没有残留，传统和自我混合混合残留模型的情况下具有最佳和高效的特点，但是忽略了主题的数量，这对优化和管理便利性都产生了重大影响。在本文中，添加了两个更实用的模型，即传统的，自我的和混合的残留模型，这些残留模型仅对新处理或测试处理具有残留效果，提出了5M算法。基于5M算法的计算机代码在五个残留模型下搜索所有可能的两种治疗交叉设计，并列出对所有五个残留模型而言最佳和/或有效的设计。