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

Authors: Sunghoon Choi; Soo-yeon Park; Hoejin Kim; Oran Kwon; Taesung Park

Addresses: Department of Statistics, Seoul National University, Seoul, South Korea ' Department of Nutritional Science & Food Management, Ewha Womans University, Seoul, South Korea ' Department of Nutritional Science & Food Management, Ewha Womans University, Seoul, South Korea ' Department of Nutritional Science & Food Management, Ewha Womans University, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea

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.

Keywords: linear mixed effect model; crossover design; repeated measurements.

DOI: 10.1504/IJDMB.2017.087170

International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.3, pp.196 - 209

Received: 19 Apr 2017
Accepted: 03 May 2017
Published online: 06 Oct 2017 *

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