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

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

by Bin LiQingzhao Yu

  • Length: 362 pages
  • Edition: 1
  • Language: English
  • Publisher: Chapman & Hall
  • Publication Date: 2022-03-14

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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