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Increasing efficiency of preclinical research by group sequential designs

Author

Listed:
  • Konrad Neumann
  • Ulrike Grittner
  • Sophie K Piper
  • Andre Rex
  • Oscar Florez-Vargas
  • George Karystianis
  • Alice Schneider
  • Ian Wellwood
  • Bob Siegerink
  • John P A Ioannidis
  • Jonathan Kimmelman
  • Ulrich Dirnagl

Abstract

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain.

Suggested Citation

  • Konrad Neumann & Ulrike Grittner & Sophie K Piper & Andre Rex & Oscar Florez-Vargas & George Karystianis & Alice Schneider & Ian Wellwood & Bob Siegerink & John P A Ioannidis & Jonathan Kimmelman & Ul, 2017. "Increasing efficiency of preclinical research by group sequential designs," PLOS Biology, Public Library of Science, vol. 15(3), pages 1-9, March.
  • Handle: RePEc:plo:pbio00:2001307
    DOI: 10.1371/journal.pbio.2001307
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    References listed on IDEAS

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    1. Carol Kilkenny & Nick Parsons & Ed Kadyszewski & Michael F W Festing & Innes C Cuthill & Derek Fry & Jane Hutton & Douglas G Altman, 2009. "Survey of the Quality of Experimental Design, Statistical Analysis and Reporting of Research Using Animals," PLOS ONE, Public Library of Science, vol. 4(11), pages 1-11, November.
    2. Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    3. Daniel Cressey, 2015. "UK funders demand strong statistics for animal studies," Nature, Nature, vol. 520(7547), pages 271-272, April.
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    1. Sophie K Piper & Ulrike Grittner & Andre Rex & Nico Riedel & Felix Fischer & Robert Nadon & Bob Siegerink & Ulrich Dirnagl, 2019. "Exact replication: Foundation of science or game of chance?," PLOS Biology, Public Library of Science, vol. 17(4), pages 1-9, April.

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