View : 3 Download: 0

Multivariate summary approach to omics data from crossover design with two repeated factors

Title
Multivariate summary approach to omics data from crossover design with two repeated factors
Authors
Choi, SunghoonPark, Soo-yeonKim, HoejinKwon, OranPark, Taesung
Ewha Authors
권오란
SCOPUS Author ID
권오란scopus
Issue Date
2017
Journal Title
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
ISSN
1748-5673JCR Link1748-5681JCR Link
Citation
vol. 18, no. 3, pp. 196 - 209
Keywords
linear mixed effect modelcrossover designrepeated measurements
Publisher
INDERSCIENCE ENTERPRISES LTD
Indexed
SCIE; SCOPUS WOS
Abstract
A crossover design, with two repeated factors, is commonly used for analysing tolerance tests, i.e., measurements of physiologic response, following ingestion of some exogenous substance. For data analysis using a crossover design, a standard approach is to use linear mixed effect models (LMMs), as these can adequately handle correlated measurements from the crossover design. Alternatively, univariate analyses, using single summary statistics, can be employed for assessments such as the difference of measurements between time points, incremental area under curve (iAUC), Cmax etc. However, the use of summary measures may result in the loss of information. In this study, instead of using one single summary measure, we propose using multiple summary measures simultaneously through LMMs by taking their correlation into account. We compare the performance of the proposed method with other existing methods through real data analysis and simulation studies. We show that our proposed method has equivalent power to that of standard LMM approach, while using a much fewer number of parameters.
DOI
10.1504/IJDMB.2017.10008262
Appears in Collections:
신산업융합대학 > 식품영양학과 > Journal papers
Files in This Item:
There are no files associated with this item.


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE