IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v10y2018i1d10.1007_s12561-017-9212-1.html
   My bibliography  Save this article

Efficient Two-Stage Designs and Proper Inference for Animal Studies

Author

Listed:
  • Chunyan Cai

    (The University of Texas Health Science Center at Houston)

  • Jin Piao

    (The University of Southern California)

  • Jing Ning

    (The University of Texas MD Anderson Cancer Center)

  • Xuelin Huang

    (The University of Texas MD Anderson Cancer Center)

Abstract

Cost-effective yet efficient designs are critical to the success of animal studies. We propose a two-stage design for cost-effectiveness animal studies with continuous outcomes. Given the data from the two-stage design, we derive the exact distribution of the test statistic under null hypothesis to appropriately adjust for the design’s adaptiveness. We further generalize the design and inferential procedure to the K-sample case with multiple comparison adjustment. We conduct simulation studies to evaluate the small sample behavior of the proposed design and test procedure. The results indicate that the proposed test procedure controls the type I error rate for the one-sample design and the family-wise error rate for K-sample design very well, whereas the naive approach that ignores the design’s adaptiveness due to the interim look severely inflates the type I error rate or family-wise error rate. Compared with the standard one-stage design, the proposed design generally requires a smaller sample size.

Suggested Citation

  • Chunyan Cai & Jin Piao & Jing Ning & Xuelin Huang, 2018. "Efficient Two-Stage Designs and Proper Inference for Animal Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 217-232, April.
  • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9212-1
    DOI: 10.1007/s12561-017-9212-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-017-9212-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12561-017-9212-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin, Pi-Erh, 1972. "Some characterizations of the multivariate t distribution," Journal of Multivariate Analysis, Elsevier, vol. 2(3), pages 339-344, September.
    2. Steve Perrin, 2014. "Preclinical research: Make mouse studies work," Nature, Nature, vol. 507(7493), pages 423-425, March.
    3. 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.
    4. Malcolm R. Macleod, 2014. "Design animal studies better," Nature, Nature, vol. 510(7503), pages 35-35, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Dean A Fergusson & Marc T Avey & Carly C Barron & Mathew Bocock & Kristen E Biefer & Sylvain Boet & Stephane L Bourque & Isidora Conic & Kai Chen & Yuan Yi Dong & Grace M Fox & Ronald B George & Neil , 2019. "Reporting preclinical anesthesia study (REPEAT): Evaluating the quality of reporting in the preclinical anesthesiology literature," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
    3. 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.
    4. Vivian Leung & Frédérik Rousseau-Blass & Guy Beauchamp & Daniel S J Pang, 2018. "ARRIVE has not ARRIVEd: Support for the ARRIVE (Animal Research: Reporting of in vivo Experiments) guidelines does not improve the reporting quality of papers in animal welfare, analgesia or anesthesi," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-13, May.
    5. Beverly S Muhlhausler & Frank H Bloomfield & Matthew W Gillman, 2013. "Whole Animal Experiments Should Be More Like Human Randomized Controlled Trials," PLOS Biology, Public Library of Science, vol. 11(2), pages 1-6, February.
    6. Rebecca Tuvel, 2015. "Against the Use of Knowledge Gained from Animal Experimentation," Societies, MDPI, vol. 5(1), pages 1-25, March.
    7. David Baker & Katie Lidster & Ana Sottomayor & Sandra Amor, 2014. "Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies," PLOS Biology, Public Library of Science, vol. 12(1), pages 1-6, January.
    8. Katarzyna Budny, 2019. "Power Generalization Of Chebyshev’S Inequality – Multivariate Case," Statistics in Transition New Series, Polish Statistical Association, vol. 20(3), pages 155-170, September.
    9. Nicole Fenwick & Peter Danielson & Gilly Griffin, 2011. "Survey of Canadian Animal-Based Researchers' Views on the Three Rs: Replacement, Reduction and Refinement," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-14, August.
    10. Carol Kilkenny & William J Browne & Innes C Cuthill & Michael Emerson & Douglas G Altman, 2010. "Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research," PLOS Biology, Public Library of Science, vol. 8(6), pages 1-5, June.
    11. Yu-Fang Chien & Haiming Zhou & Timothy Hanson & Theodore Lystig, 2023. "Informative g -Priors for Mixed Models," Stats, MDPI, vol. 6(1), pages 1-23, January.
    12. Katharina F Mueller & Matthias Briel & Daniel Strech & Joerg J Meerpohl & Britta Lang & Edith Motschall & Viktoria Gloy & Francois Lamontagne & Dirk Bassler, 2014. "Dissemination Bias in Systematic Reviews of Animal Research: A Systematic Review," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-15, December.
    13. Carlijn R Hooijmans & Rob B M de Vries & Maroeska M Rovers & Hein G Gooszen & Merel Ritskes-Hoitinga, 2012. "The Effects of Probiotic Supplementation on Experimental Acute Pancreatitis: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-12, November.
    14. Budny Katarzyna, 2019. "Power Generalization Of Chebyshev’S Inequality – Multivariate Case," Statistics in Transition New Series, Polish Statistical Association, vol. 20(3), pages 155-170, September.
    15. Stanley E Lazic & Johannes Fuss & Peter Gass, 2014. "Quantifying the Behavioural Relevance of Hippocampal Neurogenesis," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-14, November.
    16. Budny, Katarzyna, 2022. "Improved probability inequalities for Mardia’s coefficient of kurtosis," Statistics & Probability Letters, Elsevier, vol. 191(C).
    17. Larry V. Hedges & Jacob M. Schauer, 2019. "More Than One Replication Study Is Needed for Unambiguous Tests of Replication," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 543-570, October.
    18. Larry V. Hedges & Jacob M. Schauer, 2021. "The design of replication studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 868-886, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9212-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.