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

Document Type

Book

Publication Date

1-1-2022

Abstract

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

Publication Source (Journal or Book title)

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

First Page

1

Last Page

277

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