Person:
Duquesne, Sabine

Profilbild
E-Mail-Adresse
Geburtsdatum
Forschungsvorhaben
Organisationseinheiten
Logo der Organisationseinheit
Organisationseinheit
Berufsbeschreibung
Nachname
Duquesne
Vorname
Sabine
Name

Filter

20201
Filter zurücksetzen

Einstellungen

Autor:in: Sahm, René × Thema: Umweltrisikobewertung ×

Suchergebnisse

Gerade angezeigt 1 - 1 von 1
  • Vorschaubild nicht verfügbar
    Veröffentlichung
    Better define beta-optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies
    (2020) Alalouni, Urwa; Duquesne, Sabine; Egerer, Sina Elisabeth; Frische, Tobias; Gergs, René; Gräff, Thomas; Sahm, René; Pieper, Silvia; Wogram, Jörn
    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. © 2020 Springer Nature Switzerland AG