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Analysis for Doubly Repeated Omics Data from Crossover Design : 교차설계 실험에서 이중 반복 측정된 오믹스 자료 분석

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Authors

최성훈

Advisor
박태성
Major
자연과학대학 통계학과
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
Linear mixed effect model (LMM)Crossover designRepeated measurements
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 박태성.
Abstract
Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we propose LMMs to simultaneously analyze several summary measures by taking their correlations into account. We compare the performance of the proposed method with other existing methods for real doubly repeated omics data from a crossover study. We show that our method has less number of parameters but with equivalent power.
Language
English
URI
https://hdl.handle.net/10371/131335
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