Statistical methods for mediation, confounding and moderation analysis using R and SAS
Yu, Qingzhao
Statistical methods for mediation, confounding and moderation analysis using R and SAS - Boca Raton CRC Press 2022 - xv, 277 p. - Chapman & Hall/CRC Biostatistics Series .
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#)
9780367365479
Statistics - Methodology
Statistics - Data processing
519.5 / YU
Statistical methods for mediation, confounding and moderation analysis using R and SAS - Boca Raton CRC Press 2022 - xv, 277 p. - Chapman & Hall/CRC Biostatistics Series .
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#)
9780367365479
Statistics - Methodology
Statistics - Data processing
519.5 / YU