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Mathematical models (MMs) are a powerful tool to help us understand andpredict 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 experimentaltumour growth affect the outcome of the analysis. In particular, we propose aframework for including tumour volume measurements that fall outside the upperand lower limits of detection, which are normally discarded. We demonstrate howexcluding censored data results in an overestimation of the initial tumourvolume and the MM-predicted tumour volumes prior to the first measurements, andan underestimation of the carrying capacity and the MM-predicted tumour volumesbeyond the latest measurable time points. We show in which way the choice ofprior for the MM parameters can impact the posterior distributions, andillustrate that reporting the highest-likelihood parameters and their 95%credible interval can lead to confusing or misleading interpretations. We hopethis work will encourage others to carefully consider choices made in parameterestimation and to adopt the approaches we put forward herein.

A closer look at parameter identifiability, model selection and handling  of censored data with Bayesian Inference in mathematical models of tumour  growth