Brazilian Journal of Probability and Statistics

A fully Bayesian parametric approach for cytogenetic dosimetry

Carlos Daniel Paulino, Giovani L. Silva, and Márcia Branco

Full-text: Open access


This paper describes a new statistical analysis strategy to problems of cytogenetic dosimetry involving ordinal polythomous responses. Models relating the multivariate response to dose take the data ordinality into account and are analysed in a fully Bayesian fashion in the application here considered. In particular, these models are compared in order to select the best one for purposes of drawing inferences of interest and dose prediction is naturally addressed by its practical importance. This work was motivated by an in vitro experimental study on radiation exposure of human blood cell cultures, previously analysed in the literature by other methods, but its interest holds in many other applications of the biological and environmental field involving data sets yielded from the same type of assays for genetic damage.

Article information

Braz. J. Probab. Stat., Volume 27, Number 1 (2013), 70-83.

First available in Project Euclid: 16 October 2012

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Calibration categorical data continuation-ratio logits Bayesian methods nonlinear structural model


Paulino, Carlos Daniel; Silva, Giovani L.; Branco, Márcia. A fully Bayesian parametric approach for cytogenetic dosimetry. Braz. J. Probab. Stat. 27 (2013), no. 1, 70--83. doi:10.1214/11-BJPS152.

Export citation


  • Agresti, A. (2002). Categorical Data Analysis, 2nd ed. Hoboken, NJ: Wiley-Interscience.
  • Aitchison, J. and Shen, S. M. (1980). Logistic-normal distributions: Some properties and uses. Biometrika 67, 261–272.
  • Bedrick, E. J., Christensen, R. and Johnson, W. (1996). A new perspective on priors for generalized linear models. Journal of the American Statistical Association 91, 1450–1460.
  • Bender, M. A., Awa, A. A., Brooks, A. L., Evans, H. J., Groer, P. G., Littlefield, L. G., Pereira, C. A., Preston, F. J. and Wachholz, B. W. (1988). Current status of cytogenetic procedures to detect and quantify previous exposures to radiation. Mutation Research 196, 103–159.
  • Carlin, B. P. and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed. Boca Raton, FL: Chapman & Hall/CRC Press.
  • Fenech, M. (2000). The in vitro micronucleus technique. Mutation Research 455, 81–95.
  • Fenech, M. (2002). Chromosomal biomarkers of genomic instability relevant to cancer. Drug Discovery Today 7(22), 1128–1137.
  • Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman & Hall.
  • Kottas, A., Branco, M. D. and Gelfand, A. E. (2002). A nonparametric Bayesian modelling approach for cytogenetic dosimetry. Biometrics 58, 593–600.
  • Lunn, D. J., Thomas, A., Best, N. G. and Spiegelhalter, D. J. (2000). WinBUGS—a Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325–337.
  • Madruga, M. R., Pereira, C. A. and Rabello-Gay, M. N. (1994). Bayesian dosimetry: Radiation dose versus frequencies of cells with aberrations. Environmetrics 5, 47–56.
  • Madruga, M. R., Ochi-Lohmann, T. H., Okazaki, K., Pereira, C. A. and Rabello-Gay, M. N. (1996). Bayesian dosimetry II: Credibility intervals for radiation dose. Environmetrics 7, 325–331.
  • Ochi-Lohmann, T. H., Okazaki, K., Madruga, M. R., Pereira, C. A. and Rabello-Gay, M. N. (1996). Radiosensitivity of blood lymphocytes from basocellular carcinoma patients, as detected by micronucleus assay. Mutation Research 357, 97–106.
  • Osborn, C. (1991). Statistical calibration: A review. International Statistical Review 59, 309–336.
  • Paulino, C. D., Soares, P. and Neuhaus, J. (2003). Binomial regression with misclassification. Biometrics 59, 670–675.
  • Raftery, A., Newton, M., Satagopan, J. and Krivitsky, P. (2007). Estimating the integrated likelihood via posterior simulation using the harmonic mean identity (with discussion). In Bayesian Statistics 8 (J. Bernardo et al., eds.) 371–416. Oxford: Oxford Univ. Press.
  • Smith, B. J. (2007). Bayesian Output Analysis Program (BOA), version 1.1.6. Univ. Iowa. Available from
  • Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and Van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Ser. B 64, 583–616.