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Application of Group Sequential Method in the Data Analysis of Microarray Technology
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2006
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Indiana University South Bend
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Abstract
Currently, in the field of molecular biology, the application of microarray
technology is widely used for various purposes. Because huge amounts of data can be generated from microarray experiments, proper statistical experiment design and data analysis are in high demand.
Group sequential experimental design and data processing method uses interim analysis for experiments with large sample sizes. These methods can suggest early termination of the experiment with overall the same significance and desired power as the fixed sample size design or even better. As a result, with a significantly lower expected sample size, researchers will save the experimental cost with ethical and administrative benefits. This project will perform computerized simulation of different group sequential algorithms to find a desired one in order to investigate the feasibility of group sequential methods in microarray technology efficiently. R programming language will be used to simulate different group sequential algorithms. The successful codes will be used for real data from the cancer risk gene microarray study. It is expected that group sequential method will improve the experimental design strategy and data analysis for microarray echnology compared with classical fixed sample size design.
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Thesis (M.Lib.St.) Indiana University South Bend, 2006
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