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Efficient Two-Stage Designs and Proper Inference for Animal Studies

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    Chunyan Cai

  2. Keck School of Medicine, The University of Southern California, Los Angeles, CA, 90032, USA

    Jin Piao

  3. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Jing Ning & Xuelin Huang

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  1. Chunyan Cai
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  2. Jin Piao
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  3. Jing Ning
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  4. Xuelin Huang
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Correspondence to Chunyan Cai.

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Cai, C., Piao, J., Ning, J. et al. Efficient Two-Stage Designs and Proper Inference for Animal Studies. Stat Biosci 10, 217–232 (2018). https://doi.org/10.1007/s12561-017-9212-1

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  • DOI: https://doi.org/10.1007/s12561-017-9212-1

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