Department of Mathematics, Toronto Metropolitan University, Toronto, ON, M5B 2K3, Canada.
Department of Physics, Toronto Metropolitan University, Toronto, ON, M5B 2K3, Canada.
NPJ Syst Biol Appl. 2024 Aug 14;10(1):89. doi: 10.1038/s41540-024-00409-6.
Mechanistic mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the most likely parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.
机制数学模型（MMs）是一种强大的工具，可以帮助我们理解和预测在各种条件下肿瘤生长的动态。在这项工作中，我们使用了 5 个具有越来越多参数的 MMs，以探索从实验肿瘤生长数据中估计参数时的某些（通常被忽视的）决策如何影响分析的结果。特别是，我们提出了一个框架，用于包括超出检测上限和下限的肿瘤体积测量值，这些值通常会被丢弃。我们展示了排除截尾数据如何导致对初始肿瘤体积和第一次测量前的 MM 预测肿瘤体积的高估，以及对最大可测量时间点之后的承载能力和 MM 预测肿瘤体积的低估。我们展示了 MM 参数的先验选择如何影响后验分布，并说明报告最可能的参数及其 95%可信区间可能导致混淆或误导性解释。我们希望这项工作将鼓励其他人仔细考虑在参数估计中做出的选择，并采用我们在此提出的方法。
