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Keywords:

  • Calibration;
  • Cytogenetic dosimetry;
  • Dependent Dirichlet process;
  • Dose–response curve;
  • Markov chain Monte Carlo;
  • Nonparametric mixture models

Summary

We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose–response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose–response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature.