Abstract In modern pharmaceutical studies, treatments may include several drugs added sequentially, and the drugs’ order-of-addition can have significant impacts on their efficacy. In practice, experiments enumerating all possible drug sequences are often not affordable, and appropriate statistical models which can accurately predict all cases using only a small number of experimental trials are required. A novel mapping-based universal Kriging (MUK) model and its simplified variant are proposed for analyzing such order-of-addition experiments with blocking. They can provide accurate predictions and have robust performances under various experimental designs. The MUK model can also incorporate available domain knowledge to enhance its interpretation. The superiority of the proposed methods is illustrated via a real five-drug experiment on lymphoma and two simulation examples.

中文翻译：

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用于药物组合研究中添加顺序实验的基于映射的通用克里金模型

摘要 在现代药物研究中，治疗可能包括几种药物的顺序添加，药物的添加顺序对其疗效有显着影响。在实践中，列举所有可能的药物序列的实验往往是负担不起的，需要适当的统计模型，只需使用少量的实验试验即可准确预测所有病例。提出了一种新的基于映射的通用克里金 (MUK) 模型及其简化变体，用于分析这种具有分块的加法顺序实验。它们可以提供准确的预测并在各种实验设计下具有稳健的性能。MUK 模型还可以结合可用的领域知识来增强其解释。