Literature DB >> 30866739

Discovery reliability.

Anders Hånell1.   

Abstract

Whenever an experiment yields a statistically significant outcome you should ask yourself: To what extent can I trust this result? This is especially important for pre-clinical drug studies because of the frequent failures of phase III clinical trials of neurological diseases, which has put the reliability of pre-clinical research into question. Two important factors, the pre-study likelihood of treatment benefit, and statistical power, affects the reliability of the result in a quantifiable way. This can be used to assess to what extent the result of a study can be trusted (discovery reliability), and to guide the design of pre-clinical research.

Entities:  

Keywords:  Animal models; animal studies; biostatistics; clinical trial design; mathematical modeling

Mesh:

Year:  2019        PMID: 30866739      PMCID: PMC6547187          DOI: 10.1177/0271678X19837015

Source DB:  PubMed          Journal:  J Cereb Blood Flow Metab        ISSN: 0271-678X            Impact factor:   6.200


  11 in total

1.  Measuring behavior of animal models: faults and remedies.

Authors:  Ehud Fonio; Ilan Golani; Yoav Benjamini
Journal:  Nat Methods       Date:  2012-12       Impact factor: 28.547

2.  Why small low-powered studies are worse than large high-powered studies and how to protect against "trivial" findings in research: comment on Friston (2012).

Authors:  Michael Ingre
Journal:  Neuroimage       Date:  2013-04-12       Impact factor: 6.556

Review 3.  Power failure: why small sample size undermines the reliability of neuroscience.

Authors:  Katherine S Button; John P A Ioannidis; Claire Mokrysz; Brian A Nosek; Jonathan Flint; Emma S J Robinson; Marcus R Munafò
Journal:  Nat Rev Neurosci       Date:  2013-04-10       Impact factor: 34.870

4.  1,026 experimental treatments in acute stroke.

Authors:  Victoria E O'Collins; Malcolm R Macleod; Geoffrey A Donnan; Laura L Horky; Bart H van der Worp; David W Howells
Journal:  Ann Neurol       Date:  2006-03       Impact factor: 10.422

5.  Publication bias in reports of animal stroke studies leads to major overstatement of efficacy.

Authors:  Emily S Sena; H Bart van der Worp; Philip M W Bath; David W Howells; Malcolm R Macleod
Journal:  PLoS Biol       Date:  2010-03-30       Impact factor: 8.029

Review 6.  Bringing rigour to translational medicine.

Authors:  David W Howells; Emily S Sena; Malcolm R Macleod
Journal:  Nat Rev Neurol       Date:  2013-11-19       Impact factor: 42.937

7.  Statistical Rigor and the Perils of Chance.

Authors:  Katherine S Button
Journal:  eNeuro       Date:  2016-07-14

8.  Why most published research findings are false.

Authors:  John P A Ioannidis
Journal:  PLoS Med       Date:  2005-08-30       Impact factor: 11.613

9.  Evaluation of excess significance bias in animal studies of neurological diseases.

Authors:  Konstantinos K Tsilidis; Orestis A Panagiotou; Emily S Sena; Eleni Aretouli; Evangelos Evangelou; David W Howells; Rustam Al-Shahi Salman; Malcolm R Macleod; John P A Ioannidis
Journal:  PLoS Biol       Date:  2013-07-16       Impact factor: 8.029

Review 10.  Structured evaluation of rodent behavioral tests used in drug discovery research.

Authors:  Anders Hånell; Niklas Marklund
Journal:  Front Behav Neurosci       Date:  2014-07-22       Impact factor: 3.558

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