title: Estimating hazardous concentrations by an informative Bayesian approach.
abstract: The species sensitivity distribution (SSD) approach is recommended for assessing chemical risk. In practice, however, it can be used only for the few substances for which large-scale ecotoxicological results are available. Indeed, the statistical frequentist approaches used for building SSDs and for deriving hazardous concentrations (HC5) inherently require extensive data to guarantee goodness-of-fit. An alternative Bayesian approach to estimating HC5 from small data sets was developed. In contrast to the noninformative Bayesian approaches that have been tested to date, the authors' method used informative priors related to the expected species sensitivity variance. This method was tested on actual ecotoxicological data for 21 well-informed substances. A cross-validation compared the HC5 values calculated using frequentist approaches with the results of our Bayesian approach, using both complete and truncated data samples. The authors' informative Bayesian approach was compared with noninformative Bayesian methods published in the past, including those incorporating loss functions. The authors found that even for the truncated sample the HC5 values derived from the informative Bayesian approach were generally close to those obtained using the frequentist approach, which requires more data. In addition, the probability of overestimating an HC5 is rather limited. More robust HC5 estimates can be practically obtained from additional data without impairing regulatory protection levels, which will encourage collecting new ecotoxicological data. In conclusion, the Bayesian informative approach was shown to be relatively robust and could be a good surrogate approach for deriving HC5 values from small data sets.
authors.affiliation: Electricité de France, Division Recherche et Développement, Chatou, France. philippe.ciffroy@edf.fr
authors.forename: Philippe
authors.initials: P
authors.lastname: Ciffroy
authors.forename: Merlin
authors.initials: M
authors.lastname: Keller
authors.forename: Alberto
authors.initials: A
authors.lastname: Pasanisi
mesh_terms: D001499:Bayes Theorem; D004784:Environmental Monitoring / Q000379:methods*; D004787:Environmental Pollution / Q000706:statistics & numerical data*; D012107:Research Design; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity