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Brobbey A, Wiebe S, Nettel-Aguirre A, Josephson CB, Williamson T, Lix LM, Sajobi TT. Repeated measures discriminant analysis using multivariate generalized estimation equations. Stat Methods Med Res 2021; 31:646-657. [PMID: 34898331 PMCID: PMC8961244 DOI: 10.1177/09622802211032705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures.
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Affiliation(s)
- Anita Brobbey
- Department of Community Health Sciences, 2129University of Calgary, University of Calgary, Calgary, Canada
| | - Samuel Wiebe
- Department of Community Health Sciences, 2129University of Calgary, University of Calgary, Calgary, Canada.,Department of Clinical Neurosciences, 2129University of Calgary, University of Calgary, Calgary, Canada
| | - Alberto Nettel-Aguirre
- Centre for Health and Social Analytics, 8691University of Wollongong, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia
| | - Colin Bruce Josephson
- Department of Community Health Sciences, 2129University of Calgary, University of Calgary, Calgary, Canada.,Department of Clinical Neurosciences, 2129University of Calgary, University of Calgary, Calgary, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, 2129University of Calgary, University of Calgary, Calgary, Canada
| | - Lisa M Lix
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Tolulope T Sajobi
- Department of Community Health Sciences, 2129University of Calgary, University of Calgary, Calgary, Canada.,Department of Clinical Neurosciences, 2129University of Calgary, University of Calgary, Calgary, Canada
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Xu P, Peng H, Huang T. Unsupervised learning of mixture regression models for longitudinal data. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.03.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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