The CPCAT as a novel tool to overcome the shortcomings of NOEC/LOEC statistics in ecotoxicology: a simulation study to evaluate the statistical power

Application of the Closure Principle Computational Approach Test to Assess Ecotoxicological Field Studies: Comparative Analysis Using Earthworm Field Test Abundance Data

Field studies to determine the effects of chemicals on earthworm communities are generally conducted according to International Organization for Standardization standard 11268-3 (and later comments). However, statistical test procedures suggested in the guideline are frequently criticized, mainly for 2 reasons: 1) Earthworm abundances are count data and often do not fulfill requirements for multiple t tests (normal distribution and homogeneity of variance), and 2) the resulting toxicity metrics of multiple testing procedures (no/lowest-observed-effect concentrations [NOEC/LOEC]) fail to adequately detect the actual level of effects. Recently, a new method to overcome these shortcomings was presented by the introduction of the closure principle computational approach test (CPCAT). We applied this statistical method to assess chemical effects on abundance in a large dataset of 26 earthworm field studies (with up to 3 test chemical application rates) and an additional extended study with 6 application rates. A comparative analysis was provided considering results of well-established multiple testing approaches (Dunnett's test) with particular consideration of the degree of overdispersion found in these data. It was shown that the CPCAT detects substantially more effects in earthworm field tests as statistically significant than standard t test approaches. This lowered the LOEC/NOEC for many chemical treatments to control comparisons. As a consequence, the statistically detected NOECs/LOECs were often set at lower percentage deviations between control and chemical treatment. This is the first time the performance of the CPCAT has been assessed within a comprehensive analysis of earthworm field study data. Environ Toxicol Chem 2021;40:1750-1760. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Better define beta-optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies

The minimum detectable difference (MDD) is a measure of the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. It is a measure at a defined level of probability and a given variability of the data. It provides an indication for the robustness of statistically derived effect thresholds such as the lowest observed effect concentration (LOEC) and the no observed effect concentration (NOEC) when interpreting treatment-related effects on a population exposed to chemicals in semi-field studies (e.g., micro-/mesocosm studies) or field studies. MDD has been proposed in the guidance on tiered risk assessment for plant protection products in edge of field surface waters (EFSA Journal 11(7):3290, 2013), in order to better estimate the robustness of endpoints from such studies for taking regulatory decisions. However, the MDD calculation method as suggested in this framework does not clearly specify the power which is represented by the beta-value (i.e., the level of probability of type II error). This has implications for the interpretation of experimental results, i.e., the derivation of robust effect values and their use in risk assessment of PPPs. In this paper, different methods of MDD calculations are investigated, with an emphasis on their pre-defined levels of type II error-probability. Furthermore, a modification is suggested for an optimal use of the MDD, which ensures a high degree of certainty for decision-makers.