國立虎尾科技大學 |

Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling/ by George J. Knafl.
作者:	Knafl, George J.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xxv, 515 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Biostatistics. -
電子資源:	https://doi.org/10.1007/978-3-031-41988-1
ISBN:	9783031419881

Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling
Knafl, George J.

Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling[electronic resource] /by George J. Knafl. - Cham :Springer International Publishing :2023. - xxv, 515 p. :ill., digital ;24 cm.

This book formulates methods for modeling continuous and categorical correlated outcomes that extend 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 provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion 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. 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 online via the book's Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.

ISBN: 9783031419881

Standard No.: 10.1007/978-3-031-41988-1doiSubjects--Topical Terms:

783654
Biostatistics.

LC Class. No.: QA279

Dewey Class. No.: 519.5

Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling
LDR:03472nam a2200313 a 4500 001 1120766
003 DE-He213
005 20240124152137.0
006 m d
007 cr nn 008maaau
008 240612s2023 sz s 0 eng d
020 $a 9783031419881 $q (electronic bk.)
020 $a 9783031419874 $q (paper)
024 7 $a 10.1007/978-3-031-41988-1 $2 doi
035 $a 978-3-031-41988-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA279 $b .K67 2023
100 1 $a Knafl, George J. $3 1113874
245 1 0 $a Modeling correlated outcomes using extensions of generalized estimating equations and linear mixed modeling $h [electronic resource] / $c by George J. Knafl.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxv, 515 p. : $b ill., digital ; $c 24 cm.
520 $a This book formulates methods for modeling continuous and categorical correlated outcomes that extend 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 provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion 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. 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 online via the book's Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.
650 2 4 $a Biostatistics. $3 783654
650 1 4 $a Statistical Theory and Methods. $3 671396
650 0 $a Generalized estimating equations. $3 578811
650 0 $a Linear models (Statistics) $3 632956
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-41988-1
950 $a Mathematics and Statistics (SpringerNature-11649)