Isnard P, Flammarion P, Roman G, Babut M, Bastien P, Bintein S, Esserméant L, Férard J F, Gallotti-Schmitt S, Saouter E, Saroli M, Thiébaud H, Tomassone R, Vindimian E
Rhĵne-Poulenc Industrialisation, Décines Charpieu, France.
Chemosphere. 2001 Nov;45(4-5):659-69. doi: 10.1016/s0045-6535(00)00600-7.
ANOVA-type data analysis, i.e.. determination of lowest-observed-effect concentrations (LOECs), and no-observed-effect concentrations (NOECs), has been widely used for statistical analysis of chronic ecotoxicity data. However, it is more and more criticised for several reasons, among which the most important is probably the fact that the NOEC depends on the choice of test concentrations and number of replications and rewards poor experiments, i.e., high variability, with high NOEC values. Thus, a recent OECD workshop concluded that the use of the NOEC should be phased out and that a regression-based estimation procedure should be used. Following this workshop, a working group was established at the French level between government, academia and industry representatives. Twenty-seven sets of chronic data (algae, daphnia, fish) were collected and analysed by ANOVA and regression procedures. Several regression models were compared and relations between NOECs and ECx, for different values of x, were established in order to find an alternative summary parameter to the NOEC. Biological arguments are scarce to help in defining a negligible level of effect x for the ECx. With regard to their use in the risk assessment procedures, a convenient methodology would be to choose x so that ECx are on average similar to the present NOEC. This would lead to no major change in the risk assessment procedure. However, experimental data show that the ECx depend on the regression models and that their accuracy decreases in the low effect zone. This disadvantage could probably be reduced by adapting existing experimental protocols but it could mean more experimental effort and higher cost. ECx (derived with existing test guidelines, e.g., regarding the number of replicates) whose lowest bounds of the confidence interval are on average similar to present NOEC would improve this approach by a priori encouraging more precise experiments. However, narrow confidence intervals are not only linked to good experimental practices, but also depend on the distance between the best model fit and experimental data. At least, these approaches still use the NOEC as a reference although this reference is statistically not correct. On the contrary, EC50 are the most precise values to estimate on a concentration response curve, but they are clearly different from the NOEC and their use would require a modification of existing assessment factors.
方差分析类型的数据分析，即确定最低观察效应浓度（LOEC）和未观察效应浓度（NOEC），已广泛用于慢性生态毒性数据的统计分析。然而，它因多种原因受到越来越多的批评，其中最重要的可能是NOEC取决于测试浓度的选择和重复次数，并且对较差的实验（即高变异性）给予奖励，导致NOEC值较高。因此，经合组织最近的一次研讨会得出结论，应逐步淘汰NOEC的使用，并应采用基于回归的估计程序。在这次研讨会之后，法国政府、学术界和行业代表之间成立了一个工作组。收集了27组慢性数据（藻类、水蚤、鱼类），并通过方差分析和回归程序进行了分析。比较了几种回归模型，并建立了不同x值下NOEC与ECx之间的关系，以便找到一个替代NOEC的汇总参数。在定义ECx可忽略不计的效应水平x时，生物学依据很少。关于它们在风险评估程序中的使用，一种方便的方法是选择x，使ECx平均与当前的NOEC相似。这将导致风险评估程序没有重大变化。然而，实验数据表明，ECx取决于回归模型，并且它们在低效应区的准确性会降低。通过调整现有的实验方案，这个缺点可能会减少，但这可能意味着更多的实验工作和更高的成本。置信区间下限平均与当前NOEC相似的ECx（根据现有测试指南得出，例如关于重复次数），通过事先鼓励进行更精确的实验，将改进这种方法。然而，狭窄的置信区间不仅与良好的实验方法有关，还取决于最佳模型拟合与实验数据之间的距离。至少，这些方法仍然将NOEC用作参考，尽管这个参考在统计上是不正确的。相反，EC50是浓度响应曲线上最精确的估计值，但它们与NOEC明显不同，使用它们需要修改现有的评估因子。
