ArticleLaboratory animals2024
Half the price, twice the gain: How to simultaneously decrease animal numbers and increase precision with good experimental design.
Article in Laboratory animals, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- A Novel Approach to the Design and Sample Size Planning of Animal Experiments Based on Effect Estimation.Biometrical journal. Biometrische Zeitschrift · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Animal research often involves experiments in which the effect of several factors on a particular outcome is of scientific interest. Many researchers approach such experiments by varying just one factor at a time. As a consequence, they design and analyze the experiments based on a pairwise comparison between two groups. However, this approach uses unreasonably large numbers of animals and leads to severe limitations in terms of the research questions that can be answered. Factorial designs and analyses offer a more efficient way to perform and assess experiments with multiple factors of interest. We will illustrate the basic principles behind these designs, discussing a simple example with only two factors before suggesting how to design and analyze more complex experiments involving larger numbers of factors based on multiway analysis of variance.
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What OpenQuestion holds
Registered trials
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.