ArticleScientific reports2020
A multi-batch design to deliver robust estimates of efficacy and reduce animal use - a syngeneic tumour case study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Anti-PD-L1 Antibody and/or 17β-Estradiol Treatment Induces Changes in the Gut Microbiome in MC38 Colon Tumor Model.Cancer research and treatment · 2023Article
- The landscape of therapeutic vulnerabilities in EGFR inhibitor osimertinib drug tolerant persister cells.NPJ precision oncology · 2022Article
- Cancer-Associated Fibroblasts Suppress CD8+ T-cell Infiltration and Confer Resistance to Immune-Checkpoint Blockade.Cancer research · 2022Article
- Do multiple experimenters improve the reproducibility of animal studies?PLoS biology · 2022Article
- PARP inhibition is a modulator of anti-tumor immune response in BRCA-deficient tumors.Oncoimmunology · 2022Article
- What is the optimum design for my animal experiment?BMJ open science · 2021Review
- Improving reproducibility in animal research.Scientific reports · 2020Article
- Improving reproducibility in animal research by splitting the study population into several 'mini-experiments'.Scientific reports · 2020Article
- A multi-batch design to deliver robust estimates of efficacy and reduce animal use - a syngeneic tumour case study.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotypic plasticity, the ability of a living organism to respond to the environment, can lead to conclusions from experiments that are idiosyncratic to a particular environment. The level of environmental responsiveness can result in difficulties in reproducing studies from the same institute with the same standardised environment. Here we present a multi-batch approach to in-vivo studies to improve replicability of the results for a defined environment. These multi-batch experiments consist of small independent mini-experiments where the data are combined in an integrated data analysis to appropriately assess the treatment effect after accounting for the structure in the data. We demonstrate the method on two case studies with syngeneic tumour models which are challenging due to high variability both within and between studies. Through simulations and discussions, we explore several data analysis options and the optimum design that balances practical constraints of working with animals versus sensitivity and replicability. Through the increased confidence from the multi-batch design, we reduce the need to replicate the experiment, which can reduce the total number of animals used.
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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.