Department of Pharmacokinetics/Pharmacodynamics. Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, 46285, USA.
Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden.
J Pharmacokinet Pharmacodyn. 2021 Dec;48(6):815-823. doi: 10.1007/s10928-021-09771-y. Epub 2021 Jul 1.
The aim of this work was to develop and evaluate approaches of linked categorical models using individual predictions of probability. A model was developed using data from a study which assessed the perception of sweetness, creaminess, and pleasantness in dairy solutions containing variable concentrations of sugar and fat. Ordered categorical models were used to predict the individual sweetness and creaminess scores and these individual predictions were used as covariates in the model of pleasantness response. The model using individual predictions was compared to a previously developed model using the amount of fat and sugar as covariates driving pleasantness score. The model using the individual prediction of odds of sweetness and creaminess had a lower variability of pleasantness than the model using the content of sugar and fat in the test solutions, which indicates that the individual odds explain part of the variability in pleasantness. Additionally, simultaneous and sequential modeling approaches were compared for the linked categorical model. Parameter estimation was similar, but precision was better with sequential modeling approaches compared to the simultaneous modeling approach. The previous model characterizing the pleasantness response was improved by using individual predictions of sweetness and creaminess rather than the amount of fat and sugar in the solution. The application of this approach provides an advancement within categorical modeling showing how categorical models can be linked to enable the utilization of individual prediction. This approach is aligned with biology of taste sensory which is reflective of the individual perception of sweetness and creaminess, rather than the amount of fat and sugar in the solution.
这项工作的目的是开发和评估使用概率个体预测的链接分类模型方法。使用评估含变量糖和脂肪浓度的乳制品溶液的甜度、奶油度和愉悦感的研究数据开发了一个模型。有序分类模型用于预测个体的甜度和奶油度评分，这些个体预测被用作愉悦感反应模型的协变量。使用个体预测的模型与之前使用脂肪和糖含量作为驱动愉悦感评分的协变量的模型进行了比较。使用个体预测的甜度和奶油度的可能性的模型比使用测试溶液中糖和脂肪含量的模型具有更低的愉悦度可变性，这表明个体可能性解释了愉悦度可变性的一部分。此外，还比较了链接分类模型的同时和顺序建模方法。尽管参数估计相似，但与同时建模方法相比，顺序建模方法的精度更好。通过使用个体预测的甜度和奶油度而不是溶液中的脂肪和糖含量来改进描述愉悦感反应的先前模型。该方法的应用在分类模型中提供了一个进展，展示了如何链接分类模型以实现个体预测的利用。该方法与味觉感官生物学一致，反映了个体对甜度和奶油度的感知，而不是溶液中的脂肪和糖含量。
