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Analysis of data with multiple sources of correlation in the framework of generalized estimating equations
Author(s) -
Shults Justine,
Whitt Melicia C.,
Kumanyika Shiriki
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1887
Subject(s) - correlation , generalized estimating equation , statistics , regression analysis , econometrics , computer science , estimating equations , regression , mathematics , maximum likelihood , geometry
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.