Statistics in Biosciences

, Volume 10, Issue 1, pp 217–232 | Cite as

Efficient Two-Stage Designs and Proper Inference for Animal Studies

  • Chunyan CaiEmail author
  • Jin Piao
  • Jing Ning
  • Xuelin Huang


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.


Animal studies Cost effectiveness Family-wise error rate Two-stage designs 



The authors thank the editor, the associate editor, and two reviewers for their constructive comments that have greatly improved the initial version of this paper. The work was supported in part by the U.S. National Institutes of Health Grants UL1 TR000371, CA193878, and CA016672.

Supplementary material

12561_2017_9212_MOESM1_ESM.pdf (38 kb)
Supplementary material 1 (pdf 38 KB)


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Copyright information

© International Chinese Statistical Association 2017

Authors and Affiliations

  1. 1.Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational SciencesThe University of Texas Health Science Center at HoustonHoustonUSA
  2. 2.Keck School of MedicineThe University of Southern CaliforniaLos AngelesUSA
  3. 3.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA

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