Overview
- Formulates and demonstrates novel extensions of standard methods for modeling correlated outcomes
- Addresses linear, Poisson, logistic, exponential, multinomial, ordinal, and discrete regression
- Covers correlated sets of continuous, count/rate, dichotomous, polytomous, and discrete numeric outcomes
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About this book
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 necessarilydistinctly 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 online via the book’s Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.
Keywords
Table of contents (16 chapters)
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Part I
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Part III
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Authors: George J. Knafl
DOI: https://doi.org/10.1007/978-3-031-41988-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-41987-4Published: 25 January 2024
Softcover ISBN: 978-3-031-41990-4Due: 25 February 2024
eBook ISBN: 978-3-031-41988-1Published: 24 January 2024
Edition Number: 1
Number of Pages: XXV, 515
Number of Illustrations: 22 b/w illustrations, 43 illustrations in colour