Example Analyses of the Dental Measurement Data

George J. Knafl ()
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George J. Knafl: University of North Carolina at Chapel Hill, School of Nursing

Chapter Chapter 6 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2026, pp 113-148 from Springer

Abstract: Abstract Adaptive analyses are presented of dental measurements for children at 8, 10, 12, and 14 years old using linear regression with the identity link function. The choice of the number $$k$$ k of folds for computing likelihood cross-validation (LCV) scores is addressed as well as the choice of the directly specified correlation structure. Results are compared for partially modified generalized estimating equations (GEE), fully modified GEE, and linear mixed modeling (LMM). Linearity of means in child age with constant variances is addressed as well as a comparison to standard GEE modeling and the dependence of means and variances on child age. Adaptive additive and adaptive moderation models are generated for child age and child gender. A comparison to the standard linear moderation model is provided. Models based on directly specified correlation structures are compared to models based on random effects/coefficients. A summary of the analysis results is provided as well. SAS code is described for generating these analyses along with output generated by that code.

Keywords: Generalized estimating equations; Likelihood cross-validation; Linear mixed modeling; Linear regression; Moderation; Non-constant variances; Random effects/coefficients (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00989-0_6

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DOI: 10.1007/978-3-032-00989-0_6

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