R. Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, R. Mariappan, J. Rani, B. Antonisamy, Prasanna S. Premkumar
{"title":"A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research","authors":"R. Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, R. Mariappan, J. Rani, B. Antonisamy, Prasanna S. Premkumar","doi":"10.11648/j.bsi.20230802.11","DOIUrl":null,"url":null,"abstract":": 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","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Statistics and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11648/j.bsi.20230802.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: 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
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