Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Hardcover)

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

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Biostatistics Series
Release date:	February 2022
First published:	2022
Authors:	Qingzhao Yu • Bin Li
Dimensions:	234 x 156 x 23mm (L x W x T)
Format:	Hardcover
Pages:	278
ISBN-13:	978-0-367-36547-9
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Promotions
LSN:	0-367-36547-2
Barcode:	9780367365479

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