ReviewBMJ open science2021
What is the optimum design for my animal experiment?
Review in BMJ open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- From Screening to Optimization: Strategic Implementation of Design of Experiments (DOE) for Robust Nanoparticle Formulation.Polymers · 2026Review
- Silencing of the Metabolic Gene HKDC1 Is Associated With Aging and Neurodegeneration in Mice and Humans.Aging cell · 2026Article
- A call to action to address critical flaws and bias in laboratory animal experiments and preclinical research.Scientific reports · 2025Article
- Testing the reproducibility of ecological studies on insect behavior in a multi-laboratory setting identifies opportunities for improving experimental rigor.PLoS biology · 2025Article
- Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.Toxicology and applied pharmacology · 2024Article
- Standardization of zebrafish drug testing parameters for muscle diseases.Disease models & mechanisms · 2024Article
- Unmasking the Adverse Impacts of Sex Bias on Science and Research Animal Welfare.Animals : an open access journal from MDPI · 2023Review
- Group size planning for breedings of gene-modified mice and other organisms following Mendelian inheritance.Lab animal · 2023Article
- Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs.International journal of molecular sciences · 2023Article
- 3Rs Principle and Legislative Decrees to Achieve High Standard of Animal Research.Animals : an open access journal from MDPI · 2023Article
- The rearing environment persistently modulates mouse phenotypes from the molecular to the behavioural level.PLoS biology · 2022Article
- A guide to open science practices for animal research.PLoS biology · 2022Article
- Building a healthy mouse model ecosystem to interrogate cancer biology.Disease models & mechanisms · 2022Article
- Between two stools: preclinical research, reproducibility, and statistical design of experiments.BMC research notes · 2022Article
- On the past, present, and future of in vivo science.Lab animal · 2021Article
- Combining Animal Welfare With Experimental Rigor to Improve Reproducibility in Behavioral Neuroscience.Frontiers in behavioral neuroscience · 2021Review
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
Within preclinical research, attention has focused on experimental design and how current practices can lead to poor reproducibility. There are numerous decision points when designing experiments. Ethically, when working with animals we need to conduct a harm-benefit analysis to ensure the animal use is justified for the scientific gain. Experiments should be robust, not use more or fewer animals than necessary, and truly add to the knowledge base of science. Using case studies to explore these decision points, we consider how individual experiments can be designed in several different ways. We use the Experimental Design Assistant (EDA) graphical summary of each experiment to visualise the design differences and then consider the strengths and weaknesses of each design. Through this format, we explore key and topical experimental design issues such as pseudo-replication, blocking, covariates, sex bias, inference space, standardisation fallacy and factorial designs. There are numerous articles discussing these critical issues in the literature, but here we bring together these topics and explore them using real-world examples allowing the implications of the choice of design to be considered. Fundamentally, there is no perfect experiment; choices must be made which will have an impact on the conclusions that can be drawn. We need to understand the limitations of an experiment's design and when we report the experiments, we need to share the caveats that inherently exist.
Indexed as
Identifiers
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.