Example Analyses of the Epilepsy Seizure Rate Data

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

Chapter Chapter 7 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2026, pp 149-186 from Springer

Abstract: Abstract Adaptive analyses are presented in this chapter of epilepsy seizure rates per week over a baseline and four subsequent clinic visits using Poisson regression with the natural log link function. The choice of the number of folds 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 extended linear mixed modeling (ELMM). Linearity of the log of the means in visit with constant dispersions, a comparison to standard GEE modeling, and the dependence of means and dispersions on visit are addressed. Adaptive additive and adaptive moderation models are generated for visit and group (control versus intervention). An assessment of linear additive and moderation effects for the means with constant dispersions is provided as well as of direct variance modeling of seizure rates. Models based on directly specified correlation structures are compared to models based on random effects/coefficients. A summary of the analysis results is also provided. SAS code for generating these analyses is described along with output generated by that code.

Keywords: Direct variance modeling; Extended linear mixed modeling; Generalized estimating equations; Poisson regression; Moderation; Non-constant dispersions; 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_7

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DOI: 10.1007/978-3-032-00989-0_7

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