  To predict the health effects of accidental or therapeutic radiation
exposure, one must estimate the radiation dose that person received. A
well-known ionising radiation biomarker, phosphorylated gamma-H2AX protein, is
used to evaluate cell damage and is thus suitable for the dose estimation
process. In this paper, we present new Bayesian methods that, in contrast to
approaches where estimation is carried out at predetermined post-irradiation
times, allow for uncertainty regarding the time since radiation exposure and,
as a result, produce more precise results. We also use the Laplace
approximation method, which drastically cuts down on the time needed to get
results. Real data are used to illustrate the methods, and analyses indicate
that the models might be a practical choice for the gamma-H2AX biomarker dose
estimation process.
