Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series)

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

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

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