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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 Synopsis

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

About This Edition

ISBN: 9781032220086
Publication date:
Author: Qingzhao Yu, Bin Li
Publisher: Chapman & Hall/CRC an imprint of CRC Press
Format: Paperback
Pagination: 294 pages
Series: Chapman & Hall/CRC Biostatistics Series
Genres: Probability and statistics