Gujarat Commerce College, Gujarat University, Ellisbridge, Ahmedabad, India.
Department of Statistics, Sardar Patel University, Vallabh Vidyanagar, India.
J Biopharm Stat. 2020 May 3;30(3):445-461. doi: 10.1080/10543406.2019.1684311. Epub 2019 Nov 13.
The additional benefits in the analysis of crossover designs with two active treatments and a placebo motivated us to study these kinds of designs. These designs have been studied through a computer search algorithm, called 5M balanced algorithm, in two to four periods for different number of units, which resulted in optimal and/or efficient crossover designs. The new two periods crossover designs having two active treatments and a placebo, enables the estimation of treatment contrasts, unlike the classic two treatments two periods crossover which fails to estimate the treatment contrasts under self and mixed carryover model. The crossover designs having three or four periods in two active treatments and a placebo, estimate treatment contrasts more efficiently under self and mixed carryover model than the usual two treatments crossover designs. An exhaustive list of optimal and/or efficient crossover designs has been provided for designs in two periods having 6-21 subjects, three periods having 3-20 subjects and four periods having 3-14 subjects. In this list, 35 new designs are optimal for one of the established carryover models and 26 new designs are optimal and/or efficient to all four plausible carryover models.
我们对具有两种活性治疗药物和安慰剂的交叉设计的额外益处进行了分析，这促使我们研究了这类设计。我们通过一种名为“5M 平衡算法”的计算机搜索算法，对两到四个周期内不同单位数量的这些设计进行了研究，得出了最优和/或有效的交叉设计。新的两周期交叉设计有两种活性治疗药物和安慰剂，能够估计治疗对比，而经典的两治疗药物两周期交叉设计则无法在自身和混合残留模型下估计治疗对比。在自身和混合残留模型下，具有三个或四个周期的两种活性治疗药物和安慰剂的交叉设计比通常的两治疗药物交叉设计更有效地估计治疗对比。我们为两周期、六到二十一受试者，三周期、三到二十受试者和四周期、三到十四受试者的设计提供了最优和/或有效的交叉设计的详尽清单。在这份清单中，有 35 个新设计对于一种既定的残留模型是最优的，有 26 个新设计对于所有四种可能的残留模型都是最优和/或有效的。
