Efficient Two-Stage Designs and Proper Inference for Animal Studies

Chunyan Cai (), Jin Piao, Jing Ning and Xuelin Huang
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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

Statistics in Biosciences, 2018, vol. 10, issue 1, No 13, 217-232

Abstract: 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.

Keywords: Animal studies; Cost effectiveness; Family-wise error rate; Two-stage designs (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12561-017-9212-1

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