Bogacka Barbara, Latif Mahbub A H M, Gilmour Steven G, Youdim Kuresh
School of Mathematical Sciences, Queen Mary, University of London, London E1 4NS, UK.
Institute of Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh.
Biometrics. 2017 Sep;73(3):927-937. doi: 10.1111/biom.12660. Epub 2017 Jan 28.
In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate. The covariate included in the model explains some systematic variability in a random model parameter. This approach allows better understanding of the population variation as well as estimation of the model parameters with higher precision.
在本文中，我们提出了一种用于优化非线性混合效应模型实验设计的新方法，其中带有协变量信息的分类因子是与另一个设计因子相结合的设计变量。这项工作的动机源于需要为一组人肝微粒体高效设计酶动力学的临床前实验。然而，结果具有普遍性，可应用于其他实验情况，即分类因子引起的响应变化可部分由协变量解释。模型中包含的协变量解释了随机模型参数中的一些系统变异性。这种方法有助于更好地理解总体变异，并能更精确地估计模型参数。
