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Alternative Analyses

In: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling

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  • George J. Knafl

    (University of North Carolina at Chapel Hill, School of Nursing)

Abstract

Analyses are reported to identify appropriate strategies for modeling correlated outcomes in terms of available predictors, addressing which methods to use and which order to apply them. Analyses are conducted using extended linear mixed modeling (ELMM) of a continuous outcome treated as normally distributed, a count/rate outcome treated as Poisson distributed, a dichotomous outcome treated as Bernoulli distributed, a positive continuous outcome treated as exponentially distributed, and a trichotomous outcome treated as either multinomially, ordinally, or discretely distributed. Whether non-constant or unit dispersions are appropriate is addressed as well as adaptive additive modeling, adaptive moderation modeling, and a comparison of results for extended variance modeling and direct variance modeling. A detailed specification of possible analysis strategies is provided as well as an assessment of ELMM for analyzing theory-based models and future work needed.

Suggested Citation

  • George J. Knafl, 2023. "Alternative Analyses," Springer Books, in: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, chapter 0, pages 439-484, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-41988-1_15
    DOI: 10.1007/978-3-031-41988-1_15
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