Example Analyses of the Dichotomous Respiratory Status Data

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

Chapter Chapter 8 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2026, pp 187-223 from Springer

Abstract: Abstract Adaptive analyses are presented of dichotomous respiratory status levels over a baseline and four subsequent clinic visits using logistic regression with the logit 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 logits of the means in visit with constant dispersions is addressed as well as whether unit dispersions are appropriate for these data, a comparison to standard GEE, and the dependence of means and dispersions on visit. Adaptive additive and adaptive moderation models are generated for visit and being on active treatment. A comparison to the standard linear moderation model is provided as well as direct variance modeling of dichotomous respiratory status levels. 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; Logistic regression; Moderation; Non-constant dispersions; Random effects/coefficients (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-00989-0_8

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