TY - JOUR
T1 - Multivariate summary approach to omics data from crossover design with two repeated factors
AU - Choi, Sunghoon
AU - Park, Soo Yeon
AU - Kim, Hoejin
AU - Kwon, Oran
AU - Park, Taesung
N1 - Funding Information:
This work was supported by the Bio-Synergy Research Project (NRF 2013 M3A9C4078158 and NRF 2012M3A9C4048761) of the Ministry of Science, ICT and Future Planning through the National Research Foundation.
Publisher Copyright:
Copyright © 2017 Inderscience Enterprises Ltd.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Crossover design
KW - Linear mixed effect model
KW - Repeated measurements
UR - http://www.scopus.com/inward/record.url?scp=85031291306&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2017.087170
DO - 10.1504/IJDMB.2017.087170
M3 - Article
AN - SCOPUS:85031291306
SN - 1748-5673
VL - 18
SP - 196
EP - 209
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
IS - 3
ER -