Equivalence tests to support environmental biosafety decisions:theory and examples

摘要

A major role of ecological risk assessment(ERA)has been to provide scientific guidance on whether a future human activity will cause ecological harm,including such activities as release of a genetically modified organism(GMO),exotic species,or chemical pollutant into the environment.This requires the determination of the likelihoods that the activity:would cause a harm,and would not cause a harm.In the first case,the focus is on demonstrating the presence of a harm and developing appropriate management to mitigate such harm.This is usually evaluated using standard hypothesis analysis.In the second case,the focus is on demonstrating the absence of a harm and supporting a decision of biosafety.While most ERA researchers have focused on finding presence of harm,and some have wrongly associated the lack of detection of harm with biosafety,a novel approach in ERA would be to focus on demonstrating directly the safety of the activity.Although,some researchers have suggested that retrospective power analysis can be used to infer absence of harm,it actually provides inaccurate information about biosafety.A decision of biosafety can only be supported in a statistically sound manner by equivalence tests,described here.Using a 20%ecological equivalence standard in GMO examples,we illustrated the use of equivalence tests for two-samples with normal or binomial data and multi-sample normal data,and provided a spreadsheet calculator for each.In six of the eight examples,the effects of Cry toxins on a non-target organism were equivalent to a control,supporting a decision of biosafety.These examples also showed that demonstration of equivalence does not require large sample sizes.Although more relevant ecological equivalence standards should be developed to enable equivalence tests to become the main method to support biosafety decision making,we advocate their use for evaluating biosafety for non-target organisms because of their direct and accurate inference regarding safety.