A hierarchical Bayesian approach to calibrating the linear-quadratic model from clonogenic survival assay data

We propose a Bayesian hierarchical model applicable to the calibration of the linear-quadratic model of radiation dose–response. Experimental data used in model calibration were taken from a clonogenic survival assay conducted on human breast cancer cells (MDA-MB-231) across a range of radiation doses (0–6Gy). Employing Markov-chain Monte Carlo methods, we calibrated the proposed Bayesian hierarchical model, computed posterior distributions for the model parameters and survival fraction dose–response probability densities. Key contributions include the proposal of a model that incorporates multiple sources of inter- and intra-experiment variability commonly neglected in the standard frequentist approach and its subsequent application to in vitro experimental data.