Statistical methods for mediation, confounding and moderation analysis using R and SAS

By:

Yu, Qingzhao

Contributor(s):

Li, Bin

Material type: Text

TextSeries: Chapman & Hall/CRC Biostatistics SeriesPublication details: CRC Press Boca Raton 2022Description: xv, 277 pISBN:

9780367365479

Subject(s):

DDC classification:

519.5 YU

Summary: 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 (https://www.routledge.com/Statistical-Methods-for-Mediation-Confounding-and-Moderation-Analysis/Yu-Li/p/book/9780367365479#)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.5 YU (Browse shelf(Opens below))	1	Available		005497

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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

(https://www.routledge.com/Statistical-Methods-for-Mediation-Confounding-and-Moderation-Analysis/Yu-Li/p/book/9780367365479#)

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