Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield, United Kingdom.
Department of Computer Science, Faculty of Engineering, University of Sheffield, Sheffield, United Kingdom.
PLoS Comput Biol. 2024 Jul 17;20(7):e1011946. doi: 10.1371/journal.pcbi.1011946. eCollection 2024 Jul.
Dynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators' role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences.
动态系统模型通常涉及许多输入参数，需要通过敏感性分析来量化它们的“效应”和正交性，以确定哪些输入因素会导致最大的不确定性。尽管已有研究比较了全序估计量在恢复“真实”效应方面的作用，但评估它们在用于可识别性度量的稳健参数正交性方面的能力尚未得到研究。在本文中，我们进行了以下研究：（i）使用代表心血管系统的不同类别的数值模型进行评估；（ii）更广泛地评估采样方法及其与估计器的相互作用；（iii）研究估计器和采样方法的置换对输入参数正交性的影响；（iv）通过重采样研究样本收敛性；（v）评估当模型输入维度增加时，是否能够保持积极的结果。我们的研究结果表明，当与 Sobol 和格点规则采样方法结合使用时，Jansen 或 Janon 估计器表现出高效的收敛性和最小的不确定性，因此是计算参数正交性和影响的首选方法。本研究表明，全局敏感性分析是由收敛性驱动的。未收敛的指标容易出错，因此无法恢复输入参数的真实影响或正交性。这项研究通过减少相关的模糊性，阐明了估计器和采样方法的相互作用，为生命科学中的建模定义了新的实践。
