dc.description.abstract | Multiple testing problem occurs when the simultaneous inference of multiple statistical tests is of interest. Two major concerns when design with multiple testing are type I error and power. In other words, a large power under limited false positive rate is the focus. However, as the number of tests increases, the overall false positive rate increases. Traditional Bonferroni’s method is conservative and controls overall false positve rate well. Consequently, its overall power is usually small. In association studies using SNP’s as genetic markers, it is common that the statistical power becomes more important than the type I error. This thesis focuses on a two stage design for multiple hypothesis testing. I will present the exact calculation of power and false positive rate under given conditions. An efficient algorithm for computation will also be presented. Analysis of possible influential factors will be investigated through simulation studies. Recommendations for the two stage design will be suggested, including choice of the sample size and using 0.05 as the first stage significance level. | en |
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