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

Lingua: inglese

Editore: Springer, Springer International Publishing Feb 2026, 2026

303200988X / 9783032009883

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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Venditore AbeBooks dal 23 gennaio 2017

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Descrizione dell’articolo da parte del venditore

This item is printed on demand - Print on Demand Titel. Neuware -This book formulates methods for modeling continuous and categorical correlated outcomes extending the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE uses alternate estimating equations for the mean parameters. The new estimating equations in these two cases are generated by maximizing a 'likelihood' function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessian matrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models. Directly specified correlation structures are considered as well as covariance structures based on random effect/coefficients.Standard GEE, partially modified GEE, fully modified GEE, and ELMM are demonstrated and compared using a variety of regression analyses of different types of correlated outcomes. Example analyses of correlated outcomes include linear regression for continuous outcomes, Poisson regression for count/rate outcomes, logistic regression for dichotomous outcomes, exponential regression for positive-valued continuous outcome, multinomial regression for general polytomous outcomes, ordinal regression for ordinal polytomous outcomes, and discrete regression for discrete numeric outcomes. These analyses also address nonlinearity in predictors based on adaptive search through alternative fractional polynomial models controlled by likelihood cross-validation (LCV) scores. Larger LCV scores indicate better models but not necessarily distinctly better models. LCV ratio tests are used to identify distinctly better models.A SAS® macro has been developed for analyzing correlated outcomes using standard GEE, partially modified GEE, fully modified GEE, and ELMM within alternative regression contexts. This macro and code for conducting the analyses addressed in the book are available as supplementary materials upon request from the author. Detailed descriptions of how to use this macro and interpret its output are provided in the book.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 644 pp. Englisch.…

Codice articolo 9783032009883

Titolo
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Autore
George J. Knafl
Editore
Springer, Springer International Publishing Feb 2026
Anno di pubblicazione
2026
Condizione
Neu
Rilegatura
Buch
Lingua
inglese
ISBN 10
303200988X
ISBN 13
9783032009883
Edizione
seconda edizione
Peso dell'articolo
1119 grammi
Dimensioni
241x160x40 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 60 a 60 giorni lavorativiDa 60 a 60 giorni lavorativi
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