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

Sunghoon Choi, Soo Yeon Park, Hoejin Kim, Oran Kwon, Taesung Park

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)196-209
Number of pages14
JournalInternational Journal of Data Mining and Bioinformatics
Issue number3
StatePublished - 2017

Bibliographical note

Publisher Copyright:
Copyright © 2017 Inderscience Enterprises Ltd.


  • Crossover design
  • Linear mixed effect model
  • Repeated measurements


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