Comparison of Statistical Models for Cross-over design

Yonggab Kim; Kamruzzaman Md; Yeni Lim; Oran Kwon; Taesung Park

doi:10.1109/BIBM47256.2019.8983309

Comparison of Statistical Models for Cross-over design

Yonggab Kim, Kamruzzaman Md, Yeni Lim, Oran Kwon, Taesung Park

Department of Nutritional Science & Food Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Cross-over designs have been widely used in clinical trials to investigate the efficacy of new treatments. In cross-over design, each subject is treated subsequently with different treatments. Many methods such as linear mixed models (LMMs) and generalized estimating equation (GEE) models have been used to analyze the repeated measurements from cross-over design. When we consider repeated measured response variables, estimation of random components for LMMs is not always easy. In this article, we applied the GEE method to cross-over design to overcome the limitation of LMMs. To apply the GEE model to the data from the cross-over designs, we need to switch the role of variables in LMM such a way that the independent variable in LMMs is considered as a response variable in GEE model and vice versa. The purpose of this study is to compare the performance of these GEE models and LMMs for cross-over designs. Through simulation studies, we checked the type I errors and compared power to evaluate the performance of the proposed GEE model and LMMs.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1211-1213
Number of pages	3
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983309
State	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

Bibliographical note

Funding Information:
V. ACKNOWLEDGMENT This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI16C2037)

Publisher Copyright:
© 2019 IEEE.

Keywords

Correlated data
Cross-over design
Generalized estimating equation model
Mixed effect model

Access to Document

10.1109/BIBM47256.2019.8983309

Cite this

Kim, Y., Md, K., Lim, Y., Kwon, O., & Park, T. (2019). Comparison of Statistical Models for Cross-over design. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1211-1213). Article 8983309 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983309

Kim, Yonggab ; Md, Kamruzzaman ; Lim, Yeni et al. / Comparison of Statistical Models for Cross-over design. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1211-1213 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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abstract = "Cross-over designs have been widely used in clinical trials to investigate the efficacy of new treatments. In cross-over design, each subject is treated subsequently with different treatments. Many methods such as linear mixed models (LMMs) and generalized estimating equation (GEE) models have been used to analyze the repeated measurements from cross-over design. When we consider repeated measured response variables, estimation of random components for LMMs is not always easy. In this article, we applied the GEE method to cross-over design to overcome the limitation of LMMs. To apply the GEE model to the data from the cross-over designs, we need to switch the role of variables in LMM such a way that the independent variable in LMMs is considered as a response variable in GEE model and vice versa. The purpose of this study is to compare the performance of these GEE models and LMMs for cross-over designs. Through simulation studies, we checked the type I errors and compared power to evaluate the performance of the proposed GEE model and LMMs.",

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author = "Yonggab Kim and Kamruzzaman Md and Yeni Lim and Oran Kwon and Taesung Park",

note = "Funding Information: V. ACKNOWLEDGMENT This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI16C2037) Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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Kim, Y, Md, K, Lim, Y, Kwon, O & Park, T 2019, Comparison of Statistical Models for Cross-over design. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983309, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1211-1213, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983309

Comparison of Statistical Models for Cross-over design. / Kim, Yonggab; Md, Kamruzzaman; Lim, Yeni et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1211-1213 8983309 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: V. ACKNOWLEDGMENT This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI16C2037) Publisher Copyright: © 2019 IEEE.

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Y1 - 2019/11

N2 - Cross-over designs have been widely used in clinical trials to investigate the efficacy of new treatments. In cross-over design, each subject is treated subsequently with different treatments. Many methods such as linear mixed models (LMMs) and generalized estimating equation (GEE) models have been used to analyze the repeated measurements from cross-over design. When we consider repeated measured response variables, estimation of random components for LMMs is not always easy. In this article, we applied the GEE method to cross-over design to overcome the limitation of LMMs. To apply the GEE model to the data from the cross-over designs, we need to switch the role of variables in LMM such a way that the independent variable in LMMs is considered as a response variable in GEE model and vice versa. The purpose of this study is to compare the performance of these GEE models and LMMs for cross-over designs. Through simulation studies, we checked the type I errors and compared power to evaluate the performance of the proposed GEE model and LMMs.

AB - Cross-over designs have been widely used in clinical trials to investigate the efficacy of new treatments. In cross-over design, each subject is treated subsequently with different treatments. Many methods such as linear mixed models (LMMs) and generalized estimating equation (GEE) models have been used to analyze the repeated measurements from cross-over design. When we consider repeated measured response variables, estimation of random components for LMMs is not always easy. In this article, we applied the GEE method to cross-over design to overcome the limitation of LMMs. To apply the GEE model to the data from the cross-over designs, we need to switch the role of variables in LMM such a way that the independent variable in LMMs is considered as a response variable in GEE model and vice versa. The purpose of this study is to compare the performance of these GEE models and LMMs for cross-over designs. Through simulation studies, we checked the type I errors and compared power to evaluate the performance of the proposed GEE model and LMMs.

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Kim Y, Md K, Lim Y, Kwon O, Park T. Comparison of Statistical Models for Cross-over design. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1211-1213. 8983309. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983309

Comparison of Statistical Models for Cross-over design

Abstract

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Keywords

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