4.8 Article

RIPOSTE: a framework for improving the design and analysis of laboratory-based research

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ELIFE
卷 4, 期 -, 页码 -

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eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.05519

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  1. National Institute for Health Research (NIHR)
  2. Higher Education Funding Council for England (HEFCE)
  3. National Institute for Health Research (NIHR) Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
  4. UCL Institute of Ophthalmology Catey Bunce
  5. National Institute for Health Research (NIHR) Biomedical Research Centre at Guys'
  6. St. Thomas' NHS Foundation Trust
  7. King's College London
  8. British Heart Foundation (BHF) [PG/12/9/29376]
  9. Arthritis Research UK
  10. Tommy's Baby Charity
  11. GlaxoSmithKline
  12. British Medical Association (BMA)
  13. Leonard Wolfson Experimental Neurology Centre
  14. National Institute for Health Research (NIHR) Diagnostic Evidence Co-operative
  15. National Institute for Health Research (NIHR) Biomedical Research Centre at Newcastle upon Tyne Hospitals NHS Foundation Trust
  16. University of Newcastle
  17. British Heart Foundation [PG/12/9/29376] Funding Source: researchfish

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Lack of reproducibility is an ongoing problem in some areas of the biomedical sciences. Poor experimental design and a failure to engage with experienced statisticians at key stages in the design and analysis of experiments are two factors that contribute to this problem. The RIPOSTE (Reducing IrreProducibility in labOratory STudiEs) framework has been developed to support early and regular discussions between scientists and statisticians in order to improve the design, conduct and analysis of laboratory studies and, therefore, to reduce irreproducibility. This framework is intended for use during the early stages of a research project, when specific questions or hypotheses are proposed. The essential points within the framework are explained and illustrated using three examples (a medical equipment test, a macrophage study and a gene expression study). Sound study design minimises the possibility of bias being introduced into experiments and leads to higher quality research with more reproducible results.

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