Mathematics

Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models

J. Porthiyas, D. Nussey, et al.

This paper, conducted by Jamie Porthiyas, Daniel Nussey, Catherine A. A. Beauchemin, Donald C. Warren, Christian Quirouette, and Kathleen P. Wilkie, uncovers the pivotal role of decision-making in parameter estimation from experimental tumor growth data, proposing a framework that handles censored data to enhance analysis accuracy.

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Abstract

This paper explores the impact of various decisions in estimating parameters from experimental tumour growth data on the outcome of the analysis using five mechanistic mathematical models (MMs) with increasing complexity. A framework is proposed to include censored data (measurements outside detection limits), demonstrating how its exclusion leads to biased estimations of initial tumour volume, carrying capacity, and model-predicted tumour volumes. The influence of prior choices on posterior distributions is highlighted, emphasizing the potential for misleading interpretations when only reporting most likely parameters and their 95% credible intervals.

Publisher

npj Systems Biology and Applications

Published On

Aug 14, 2024

Authors

Jamie Porthiyas, Daniel Nussey, Catherine A. A. Beauchemin, Donald C. Warren, Christian Quirouette, Kathleen P. Wilkie

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