Bayesian dosimetry: Radiation dose versus frequencies of cells with aberrations

Madruga, Maria Regina; Pereira, Carlos Alberto de Bragança; Rabello-Gay, Maria Nazareth

doi:10.1002/env.3170050105

Cited by 7 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The case of binary response yields the familiar bioassay problem. In the cytogenetic dosimetry literature, the polytomous response case has been examined, as in, e.g., Madruga, Pereira, and Rabello-Gay (1994) and Madruga et al (1996) but exclusively under parametric models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Nonparametric Bayesian Modeling Approach for Cytogenetic Dosimetry

2002

View full text Add to dashboard Cite

In cytogenetic dosimetry, samples of cell cultures are exposed to a range of doses of a given agent. In each sample at each dose level, some measure of cell disability is recorded. The objective is to develop models that explain cell response to dose. Such models can be used to predict response at unobserved doses. More important, such models can provide inference for unknown exposure doses given the observed responses. Typically, cell disability is viewed as a Poisson count, but in the present work, a more appropriate response is a categorical classification. In the literature, modeling in this case is very limited. What exists is purely parametric. We propose a fully Bayesian nonparametric approach to this problem. We offer comparison with a parametric model through a simulation study and the analysis of a real dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Such modeling cannot handle prediction or inversion. In order to do so, Madruga et al (1994) introduce a parametric model for the log-ratio transformed probabilities as a function of dose.…”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Bayesian Modeling Approach for Cytogenetic Dosimetry

2002

View full text Add to dashboard Cite

show abstract

Bayesian Dosimetry Ii: Credibility Intervals for Radiation Dose

Madruga¹,

Ochi-Lohmann

Okazaki

et al. 1996

Environmetrics

Self Cite

View full text Add to dashboard Cite

SUMMARYThis paper is the natural sequel to Madruga ef al. (Environmetrics, 547-56 (1994)) where data from literature were analysed. Here, three sets of experimental data were obtained at IPEN (Energetic and Nuclear Research Institute) from blood cultures from three patients with basal cellular carcinoma (1 male, age 68, and 2 females, ages 47 and 66), four healthy young subjects (2 males, ages 24 and 27, and 2 females, ages 20 and 27) and two older healthy subjects (1 male, age 50, and 1 female, age 46). These cultures were exposed in vitro to ' %o radiati0.n ranging from 0 to 500cGy. The frequencies of cells with one or two nuclei, with or without micronuclei (MN), are the responses. Models for mono-and binucleated cells were obtained. As suggested in Madruga et al., models for mononucleated are simpler than for binucleated cells. The novelty is that, based on the observed frequencies of micronucleated cells, we can generate credible intervals for unknown doses to which an individual was exposed. The purpose of this paper is to obtain calibration models that will be helpful in establishing credible intervals for unknown exposure doses of a given agent once we have the observed responses. The agent in our case is radiation and the responses are frequencies of mono-and binucleated cells with none, one and two or more micronuclei.The underline probability distribution of the vector of these frequencies, yi = ( y a , yil, yi2), for each dose i, and for each type of cell (mono-and binucleated), is the trinomial distribution with parameter vector xi = ( R~, nil, 7ri2), where 7rio + 7ril + 7ri2 = 1. Recall that yi0 is the frequency of cells with no MN, yil with one, and yi2 with two or more MN.The Bayesian approach is used taking the conjugate Dirichlet distribution as the prior. The CCC 1 180-4009/96/030325-07

show abstract