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Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
Paperback
Series: Chapman & Hall/CRC Biostatistics
ISBN13: 9781032220086
Publisher: CRC Press
Published: May 27 2024
Pages: 294
Weight: 0.91
Height: 0.62 Width: 6.14 Depth: 9.21
Language: English
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
1 different editions
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Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
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Li, Bin
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Yu, Qingzhao
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
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