TY - JOUR
T1 - The Precautionary Principle and statistical approaches to uncertainty
AU - Keiding, Niels
AU - Budtz-Jørgensen, Esben
N1 - Keywords: Bayes Theorem; Benchmarking; Denmark; Environmental Health; Environmental Monitoring; Environmental Pollutants; Humans; Models, Statistical; Primary Prevention; Reproducibility of Results; Risk Assessment; Sensitivity and Specificity; Uncertainty
PY - 2004
Y1 - 2004
N2 - The central challenge from the Precautionary Principle to statistical methodology is to help delineate (preferably quantitatively) the possibility that some exposure is hazardous, even in cases where this is not established beyond reasonable doubt. The classical approach to hypothesis testing is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model uncertainty: usually these procedures assume that the class of models describing dose/response is known with certainty; this assumption is, however, often violated, perhaps particularly often when epidemiological data form the source of the risk assessment, and regulatory authorities have occasionally resorted to some average based on competing models. The recent methodology of the Bayesian model averaging might be a systematic version of this, but is this an arena for the Precautionary Principle to come into play?
AB - The central challenge from the Precautionary Principle to statistical methodology is to help delineate (preferably quantitatively) the possibility that some exposure is hazardous, even in cases where this is not established beyond reasonable doubt. The classical approach to hypothesis testing is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model uncertainty: usually these procedures assume that the class of models describing dose/response is known with certainty; this assumption is, however, often violated, perhaps particularly often when epidemiological data form the source of the risk assessment, and regulatory authorities have occasionally resorted to some average based on competing models. The recent methodology of the Bayesian model averaging might be a systematic version of this, but is this an arena for the Precautionary Principle to come into play?
M3 - Journal article
C2 - 15212218
SN - 1232-1087
VL - 17
SP - 147
EP - 151
JO - International Journal of Occupational Medicine and Environmental Health
JF - International Journal of Occupational Medicine and Environmental Health
IS - 1
ER -