Statistical methods for mediation, confounding and moderation analysis using R and SAS / by Qingzhao Yu, Bin Li.

1 Introduction � 2 A Review of Third-Variable Effect Inferences � 3 Advanced Statistical Modeling and Machine Learning Methods Used in the Book � 4 The General Third-Variable Effect Analysis Method � 5 The Implementation of General Third-Variable Effect Analysis Method � 6 Assumptions for the General Third-Variable Analysis � 7 Multiple Exposures and Multivariate Responses � 8 Regularized Third-Variable Effect Analysis for High-Dimensional Dataset � 9 Interaction/Moderation Analysis with Third-Variable Effects� 10 Third-Variable Effect Analysis with Multilevel Additive Models � 11 Bayesian Third-Variable Effect Analysis � 12 Other Issues

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

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