Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
INSTITUT ROCHE, 30, cours de l'île Seguin, 92650, Boulogne-Billancourt, France.
AAPS J. 2018 Mar 29;20(3):56. doi: 10.1208/s12248-018-0205-x.
In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.
在药物开发中，药物代谢动力学方法包括通过模型选择（MS）过程来确定最能描述数据的模型结构。然而，使用选定的模型进行预测忽略了模型结构的不确定性，这可能会影响预测性能。为了克服这一缺点，模型平均（MA）通过将候选模型集的权重作为信息准则的函数来考虑不确定性。我们的主要目标是使用临床试验模拟（CTS），基于 AIC 信息准则，比较剂量发现临床试验中的模型选择（MS）和模型平均（MA）。该分析的次要目的是通过使用五个不同的信息准则比较 MA 和 MS，来挑战 AIC 的使用。CTS 基于描述湿性年龄相关性黄斑变性患者视力随时间变化的非线性混合效应模型。使用三个关注 II 期临床试验主要目标的性能标准来评估建模方法的预测性能。在这种情况下，MA 充分描述了数据，并且比 MS 具有更好的预测性能，从而更有可能准确描述剂量反应关系并确定最小有效剂量。此外，无论采用哪种建模方法，AIC 都与最佳预测性能相关。
