Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS - Couverture rigide

Yu, Qingzhao; Li, Bin

Couverture rigide

ISBN 10 : 0367365472 ISBN 13 : 9780367365479

Editeur : Chapman and Hall/CRC, 2022

Synopsis

Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.

Key Features:

• Parametric and nonparametric method in third variable analysis
• Multivariate and Multiple third-variable effect analysis
• Multilevel mediation/confounding analysis
• Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis
• R packages and SAS macros to implement methods proposed in the book

A propos du livre

À propos de l'auteur

Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.

Bin Li is Associate Professor in Statistics, Louisiana State University.

Détails de l'édition

• Éditeur: Chapman and Hall/CRC
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 0367365472
• ISBN 13: 9780367365479
• Reliure: Relié
• Nombre de pages: 277
• Coordonnées du fabricant: non disponible
• Personne responsable: gpsr@libri.de; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Allemagne