1 research outputs found
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth
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 highest-likelihood 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.Comment: 15 pages, 7 figure