Evidence map›Paper›PMID 25724152›Full record

ArticleArchives of toxicology2016

Enhancing the interpretation of statistical P values in toxicology studies: implementation of linear mixed models (LMMs) and standardized effect sizes (SESs).

Kerstin Schmidt, Jörg Schmidtke, Christian Kohl, Ralf Wilhelm, Joachim Schiemann, Hilko van der Voet, Pablo Steinberg

Abstract read
In one paragraph

Article in Archives of toxicology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kerstin SchmidtBioMath GmbH, Schnickmannstr. 4, 18055, Rostock, Germany. kerstin.schmidt@biomath.de.
Jörg SchmidtkeBioMath GmbH, Schnickmannstr. 4, 18055, Rostock, Germany.
Christian KohlInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Erwin-Baur-Str. 27, 06484, Quedlinburg, Germany.
Ralf WilhelmInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Erwin-Baur-Str. 27, 06484, Quedlinburg, Germany.
Joachim SchiemannInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Erwin-Baur-Str. 27, 06484, Quedlinburg, Germany.
Hilko van der VoetBiometris, Wageningen UR, P.O. Box 16, 6700 AA, Wageningen, The Netherlands.
Pablo SteinbergInstitute for Food Toxicology and Analytical Chemistry, University of Veterinary Medicine Hannover, Bischofsholer Damm 15, 30173, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we compare the traditional ANOVA approach to analysing data from 90-day toxicity studies with a more modern LMM approach, and we investigate the use of standardized effect sizes. The LMM approach is used to analyse weight or feed consumption data. When compared to the week-by-week ANOVA with multiple test results per week, this approach results in only one statement on differences in weight development between groups. Standardized effect sizes are calculated for the endpoints: weight, relative organ weights, haematology and clinical biochemistry. The endpoints are standardized, allowing different endpoints of the same study to be compared and providing an overall picture of group differences at a glance. Furthermore, in terms of standardized effect sizes, statistical significance and biological relevance are displayed simultaneously in a graph.

Indexed as

Linear ModelsAnalysis of VarianceAnimalsBody WeightMaleOrgan SizePlants, Genetically ModifiedRatsToxicity TestsToxicologyZea mays90-day toxicity studyANOVALinear mixed modelsP valuesRepeated measurementsStandardized effect sizes

Identifiers

PMID25724152
PMCPMC4754325

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.