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

<p>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.</p><p><b><i>Statistical Methods for Mediation Confounding and Moderation Analysis Using R and SAS </i></b>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.</p><p>Key Features:</p><ul> <li>Parametric and nonparametric method in third variable analysis</li> <li>Multivariate and Multiple third-variable effect analysis</li> <li>Multilevel mediation/confounding analysis</li> <li>Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis</li> <li>R packages and SAS macros to implement methods proposed in the book</li> </ul>