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Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series)

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series)

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R
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Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series) Hardback - 2022

by Yu, Qingzhao/ Li, Bin

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Chapman and Hall/CRC, 2022. Hardcover. New. 277 pages. 9.25x6.25x0.75 inches.
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Details

  • Title Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series)
  • Author Yu, Qingzhao/ Li, Bin
  • Binding Hardback
  • Condition New
  • Pages 294
  • Volumes 1
  • Language ENG
  • Publisher Chapman and Hall/CRC
  • Publication date 2022
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # x-0367365472
  • ISBN 9780367365479 / 0367365472
  • Weight 1.3 lbs (0.59 kg)
  • Dimensions 9.21 x 6.14 x 0.69 in (23.39 x 15.60 x 1.75 cm)
  • Category Mathematics
  • Library of Congress subjects Statistics - Data processing, SAS (Computer program language)
  • Library of Congress Catalogue Number 2021046570
  • Dewey Decimal Code 519.502
  • Quantity available 2

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From the publisher

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 the author

Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.

Bin Li is Associate Professor in Statistics, Louisiana State University.

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