A Bayesian Poisson mixture model for model selection and dose estimation in biological retrospective dosimetry

Abstract

International audienceScoring of dicentric chromosome aberrations in peripheral blood lymphocytes is considered to be the "gold-standard" biological method to estimate the radiation dose received by individuals after proven radiation accidents. In this specific context, two main questions arise: 1) "Given the number of dicentrics observed in some blood lymphocytes of a given individual, what are the estimated absorbed dose and its associated uncertainty?"and 2) "Was the radiation exposure total or partial?" Dose estimation is highly relevant to optimize patient-centered care and predict the health consequences of radiation exposure. Moreover, dose estimation from dicentric counts can be crucial to clarify unclear radiation exposure scenarios. In this context, one important question is: 3) "Given the number of dicentrics observed, was the individual really exposed to ionizing radiation?" Frequentist statistical approaches are commonly used to answer the above questions that are then formalized as hypotheses testing and inverse regression problems but no consensus has been reached so far on the best way to proceed. In this work, we propose an alternative approach based on the Bayesian inference of a Poisson mixture model. This approach allows providing, in a unique and coherent framework, some rich and simultaneous probabilistic answers to the above three questions. Particularly, our mixture model is used as a Bayesian model selection tool that is relevant to answer questions 2) and 3). A specific adaptive Metropolis-Hastings algorithm was implemented to avoid potential convergence difficulties when estimating the mixture weights. Using simulation studies and cytogenetic data from real radiation accident victims, we discuss the advantages of the proposed Bayesian approach compared to the classical ones. A sensitivity analysis to the prior choice on the unknown dose and the mixture weights was also performed