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Efficient two-stage designs and proper inference for animal studies.

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.

SUBMITTER: Cai C

PROVIDER: S-EPMC6168085 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Efficient two-stage designs and proper inference for animal studies.

Cai Chunyan C Piao Jin J Ning Jing J Huang Xuelin X

Statistics in biosciences 20171213 1

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 <i>K</i>-sample case with multiple comparison adjustment. We conduct simulation st ...[more]

PMID: 30294384

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Project description:Large-scale whole genome association studies are increasingly common, due in large part to recent advances in genotyping technology. With this change in paradigm for genetic studies of complex diseases, it is vital to develop valid, powerful, and efficient statistical tools and approaches to evaluate such data. Despite a dramatic drop in genotyping costs, it is still expensive to genotype thousands of individuals for hundreds of thousands single nucleotide polymorphisms (SNPs) for large-scale whole genome association studies. A multi-stage (or two-stage) design has been a promising alternative: in the first stage, only a fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in later stages. Multi-stage designs have also been used in candidate gene association studies, usually in regions that have shown strong signals by linkage studies. To decide which set of SNPs to be genotyped in the next stage, a common practice is to utilize a simple test (such as a chi2 test for case-control data) and a liberal significance level without corrections for multiple testing, to ensure that no true signals will be filtered out. In this paper, I have developed a novel SNP selection procedure within the framework of multi-stage designs. Based on data from stage 1, the method explicitly explores correlations (linkage disequilibrium) among SNPs and their possible interactions in determining the disease phenotype. Comparing with a regular multi-stage design, the approach can select a much reduced set of SNPs with high discriminative power for later stages. Therefore, not only does it reduce the genotyping cost in later stages, it also increases the statistical power by reducing the number of tests. Combined analysis is proposed to further improve power, and the theoretical significance level of the combined statistic is derived. Extensive simulations have been performed, and results have shown that the procedure can reduce the number of SNPs required in later stages, with improved power to detect associations. The procedure has also been applied to a real data set from a genome-wide association study of the sporadic amyotrophic lateral sclerosis (ALS) disease, and an interesting set of candidate SNPs has been identified.

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Efficient two-stage designs and proper inference for animal studies.

Publications

Efficient two-stage designs and proper inference for animal studies.

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