Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1135-1149 |
| Seitenumfang | 15 |
| Fachzeitschrift | Archives of toxicology |
| Jahrgang | 94 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 19 März 2020 |
| Publikationsstatus | Veröffentlicht - Apr. 2020 |
Abstract
The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration–response and to identify a threshold with a biologically relevant and probably non-random deviation from “normal”. Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.
ASJC Scopus Sachgebiete
- Pharmakologie, Toxikologie und Pharmazie (insg.)
- Toxikologie
- Umweltwissenschaften (insg.)
- Gesundheit, Toxikologie und Mutagenese
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in: Archives of toxicology, Jahrgang 94, Nr. 4, 04.2020, S. 1135-1149.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Alternatives to statistical decision trees in regulatory (eco-)toxicological bioassays
AU - Kluxen, Felix M.
AU - Hothorn, Ludwig A.
PY - 2020/4
Y1 - 2020/4
N2 - The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration–response and to identify a threshold with a biologically relevant and probably non-random deviation from “normal”. Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.
AB - The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration–response and to identify a threshold with a biologically relevant and probably non-random deviation from “normal”. Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.
KW - Assumption tests
KW - Hazard characterization
KW - Hazard identification
KW - Pre-tests
KW - Regulatory toxicology
KW - Robust statistics
UR - http://www.scopus.com/inward/record.url?scp=85082875445&partnerID=8YFLogxK
U2 - 10.1007/s00204-020-02690-w
DO - 10.1007/s00204-020-02690-w
M3 - Article
C2 - 32193567
AN - SCOPUS:85082875445
VL - 94
SP - 1135
EP - 1149
JO - Archives of toxicology
JF - Archives of toxicology
SN - 0340-5761
IS - 4
ER -