Evidence map›Paper›PMID 42292834›Full record

ReviewFrontiers in pharmacology2026

Reduce or not reduce: a practical guide to calculate sample size for various experimental design in studies involving animals.

Jean Xavier Mazoit, Patrick Gonin

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Jean Xavier MazoitAnimal Ethics Committee CEEA26 (Comité d'Ethique en Expérimentale Animale 26), Université Paris-Saclay, Villejuif, France.
Patrick GoninAnimal Ethics Committee CEEA26 (Comité d'Ethique en Expérimentale Animale 26), Université Paris-Saclay, Villejuif, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ethical imperative to reduce the use of animals in scientific experimentation has become increasingly prominent, driven by societal expectations, regulatory frameworks, and concerns regarding the reproducibility of preclinical research. While the principle of Reduction, one of the 3Rs originally defined by Russell and Burch, is widely endorsed, its practical implementation remains challenging. In particular, inappropriate experimental designs, inadequate statistical planning, and insufficient reporting standards continue to undermine both scientific validity and ethical justification, often leading either to the unnecessary use of animals or to underpowered studies whose results lack reliability. This review provides an updated and practical framework for determining the appropriate number of animals required in experimental biology and pharmacology. Building on earlier methodological recommendations, it emphasizes the central role of rigorous study planning, including the clear definition of objectives and primary endpoints, appropriate experimental design, randomization, blinding, and the judicious use of control groups. Special attention is given to factorial designs and the inclusion of interactions, which often allow substantial reductions in animal numbers without compromising statistical power. The review also discusses conditional procedures, pilot studies, interim analyses, and the frequent confusion between biological replicates, technical replicates, and true experimental replication. The statistical foundations of

Indexed as

animal experimentationexperimentaldesignpharmacokinetics and pharmacodynamicspreclinicalresearchreduction principle (3Rs)sample size calculationstatistical power

Identifiers

PMID42292834
PMCPMC13254570

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.