@inproceedings{bad07541eff2432987233363cb26d451,
title = "Analysis for doubly repeated omics data from crossover design",
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 compare the performance of both methods for real doubly repeated omics data from a crossover study.",
keywords = "Crossover design, Linear mixed effect model, Repeated measurements",
author = "Sunghoon Choi and Park, {Soo Yeon} and Hoejin Kim and Oran Kwon and Taesung Park",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; null ; Conference date: 15-12-2016 Through 18-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/BIBM.2016.7822782",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1749--1752",
editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, {Xiaohua Tony} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",
booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",
}