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Abstract：
In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol\'s GSA assuming no correlations, Sobol\'s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol\'s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.
摘要：
在药物开发中，决策通常通过基于模型的方法来支持，例如基于生理的药代动力学(PBPK)。全球敏感性分析(GSA)正在用于模型知情推断的质量评估。然而,GSA中相关因素的纳入和解释已被证明是一个问题。在这里，我们开发并评估了一种用于处理GSA中相关因素的潜在变量方法。开发了一种方法，该方法通过三个独立因素的因果关系来描述两个模型输入之间的相关性：潜在变量和两个相关参数的唯一方差。潜在变量方法应用于一组代数模型和PBPK的案例。然后，将该方法与Sobol的GSA进行比较，假设没有相关性，Sobol的GSA与团体和Kucherenko方法。对于潜在变量方法，GSA用Sobol方法进行。通过使用潜在变量方法，有可能设计出一种独特而简单的敏感性指数解释，同时保持因素之间的相关性。比较方法要么考虑参数独立，将因变量分组为一个独特的因素，或者在敏感性指数的解释中存在困难。在要求GSA支持模型知情决策的情况下，潜在变量方法提供了一种实用的方法，在易于实施和可解释性方面，将GSA应用于具有不违反独立性假设的相关输入的模型。讨论了该方法的先决条件和局限性。
