Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models.
NPJ Syst Biol Appl
; 10(1): 89, 2024 Aug 14.
Article
em En
| MEDLINE
| ID: mdl-39143084
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Neoplasias
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article