Evidence map›Paper›PMID 26473839›Full record

ReviewInternational journal of molecular sciences2015

The Potential of Adaptive Design in Animal Studies.

Arshad Majid, Ok-Nam Bae, Jessica Redgrave, Dawn Teare, Ali Ali, Daniel Zemke

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
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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

6 authors.

Arshad MajidSheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK. arshad.majid@sheffield.ac.uk.
Ok-Nam BaeCollege of Pharmacy Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan 426-791, Korea. onbae@hanyang.ac.kr.
Jessica RedgraveSheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK. Jessica.Redgrave@sth.nhs.uk.
Dawn TeareSchool of Health and Related Research, University of Sheffield, Sheffield S10 2HQ, UK. m.d.teare@sheffield.ac.uk.
Ali AliSheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK. Ali.Ali@sth.nhs.uk.
Daniel ZemkeSheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK. zemkedan@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical trials are the backbone of medical research, and are often the last step in the development of new therapies for use in patients. Prior to human testing, however, preclinical studies using animal subjects are usually performed in order to provide initial data on the safety and effectiveness of prospective treatments. These studies can be costly and time consuming, and may also raise concerns about the ethical treatment of animals when potentially harmful procedures are involved. Adaptive design is a process by which the methods used in a study may be altered while it is being conducted in response to preliminary data or other new information. Adaptive design has been shown to be useful in reducing the time and costs associated with clinical trials, and may provide similar benefits in preclinical animal studies. The purpose of this review is to summarize various aspects of adaptive design and evaluate its potential for use in preclinical research.

Indexed as

Research DesignAnimalsBiomedical ResearchClinical Trials as TopicDrug Evaluation, Preclinicaladaptive designanimal studiespreclinical research

Identifiers

PMID26473839
PMCPMC4632737

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.