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A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research

In repeated measures data, the observations tend to be correlated within each subject, and such data are often analyzed using Generalized Estimating Equations (GEE), which are robust to assumptions that many methods hold. The main limitation of GEE is that its method of estimation is quasi-likelihood. The recent framework of the copula is very popular for handling repeated data. The maximum likelihood-based analysis for repeated data can be obtained using Gaussian copula regression. The purpose of this study is to show the handling and analysis of the repeated data using the Gaussian copula regression approach and compare the findings with GEE. The prospective, double-blinded, randomized controlled trial data for this study was obtained from the Department of Anesthesia, Christian Medical College, and Vellore. ASA I and II patients were randomized into three groups. Hemodynamic parameters were obtained for 88 patients at thirteen-time points. The outcome of interest was mean arterial pressure. Both GEE and Gaussian copula regression were compared assuming four different correlation structures. The optimal correlation structures were selected with the Akaike Information Criterion (AIC) and Correlation Information Criterion (CIC) goodness of fit criteria according to the method of estimation of Gaussian copula regression and GEE, respectively. The correlation structures, unstructured and autoregressive, were found to be optimal for Gaussian copula regression and GEE based on AIC and CIC criteria values respectively. A comparison between the estimated values of the selected models showed no major differences. Gaussian copula regression found that intrathecal morphine has a significant reduction in MAP over time, this significance is considered important as the study uses randomized controlled trial data. Both methods have almost similar results, but Gaussian copula regression provides better results by identifying significant findings associated with the outcome using maximum likelihood estimation that GEE fails to identify using quasi-likelihood estimation.

Correlation Structures, Gaussian Copula Regression, Generalized Estimating Equations, Repeated Data

APA Style

Reka Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, Ramamani Mariappan, Jansi Rani, et al. (2023). A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research. Biomedical Statistics and Informatics, 8(2), 22-30. https://doi.org/10.11648/j.bsi.20230802.11

ACS Style

Reka Karuppusami; Gomathi Sudhakar; Juliya Pearl Joseph Johnson; Ramamani Mariappan; Jansi Rani, et al. A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research. Biomed. Stat. Inform. 2023, 8(2), 22-30. doi: 10.11648/j.bsi.20230802.11

AMA Style

Reka Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, Ramamani Mariappan, Jansi Rani, et al. A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research. Biomed Stat Inform. 2023;8(2):22-30. doi: 10.11648/j.bsi.20230802.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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