Analysis of data with multiple sources of correlation in the framework of generalized estimating equations

• Published in: Statistics in medicine Vol. 23; no. 20; pp. 3209 - 3226
• Main Authors: Shults, Justine, Whitt, Melicia C., Kumanyika, Shiriki
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.10.2004; Wiley Subscription Services, Inc
• Subjects: African Americans; Algorithms; Biometry - methods; Black or African American; Black People; correlated data; Data analysis; Exercise - physiology; Female; generalized estimating equations; Humans; Kronecker product; Least-Squares Analysis; Models, Statistical; Motor Activity - physiology; multi-level correlated data; Physical fitness; quasi-least squares; Statistics; Surveys and Questionnaires; Women
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.1887

This paper is motivated by a study of physical activity participation habits in African American women with three potential sources of correlation among study outcomes, according to method of assessment, timing of measurement, and intensity of physical activity. To adjust for the multiple sources of correlation in this study, we implement an approach based on generalized estimating equations that models association via a patterned correlation matrix. We present a general algorithm that is relatively straightforward to program, an analysis of our physical activity study, and some asymptotic relative efficiency comparisons between correctly specifying the correlation structure vs ignoring two sources of correlation in the analysis of data from this study. The efficiency comparisons demonstrate that correctly modeling the correlation structure can prevent substantial losses in efficiency in estimation of the regression parameter. Copyright © 2004 John Wiley & Sons, Ltd.