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A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models. (English) Zbl 1419.62351

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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
62G08 Nonparametric regression and quantile regression

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References:

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