Evidence map›Paper›PMID 27480578›Full record

ArticleScientific reports2016

Optimized design and analysis of preclinical intervention studies in vivo.

Teemu D Laajala, Mikael Jumppanen, Riikka Huhtaniemi, Vidal Fey, Amanpreet Kaur, Matias Knuuttila, Eija Aho, Riikka Oksala, Jukka Westermarck, Sari Mäkelä and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Review
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  6. Article
  7. Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Characterization of a xenograft model for anti-CD19 CAR T cell studies.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

12 authors.

Teemu D LaajalaDepartment of Mathematics and Statistics, University of Turku, Turku, Finland.
Mikael JumppanenTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Riikka HuhtaniemiTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Vidal FeyTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Amanpreet KaurCancer Cell Signaling Group, Turku Centre for Biotechnology, University of Turku, Turku, Finland.
Matias KnuuttilaTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Eija AhoOrion Corporation, Orion Pharma, Department of Oncology and Critical Care Research, Turku, Finland.
Riikka OksalaDrug Research Doctoral Programme (DRDP), University of Turku, Turku, Finland.
Jukka WestermarckCancer Cell Signaling Group, Turku Centre for Biotechnology, University of Turku, Turku, Finland.
Sari MäkeläTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Matti PoutanenTurku Center for Disease Modeling (TCDM), University of Turku, Turku, Finland.
Tero AittokallioDepartment of Mathematics and Statistics, University of Turku, Turku, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.

Indexed as

Research DesignAnimalsHumansMaleMiceModels, StatisticalNeoplasm TransplantationProstatic NeoplasmsRandom AllocationReproducibility of ResultsSample SizeSequence Analysis, RNASoftware

Identifiers

PMID27480578
PMCPMC4969752

What OpenQuestion holds

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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.