Yu Q, Li B. Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

CRC, 2022. - 294 p. - ISBN: 0367365472The 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 FeaturesThe 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